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Mastering Hadoop, Part 3: Hadoop Ecosystem: Get the most out of your cluster

As we have already seen with the basic components (Part 1, Part 2), the Hadoop ecosystem is constantly evolving and being optimized for new applications. As a result, various tools and technologies have developed over time that make Hadoop more powerful and even more widely applicable. As a result, it goes beyond the pure HDFS & MapReduce platform and offers, for example, SQL, as well as NoSQL queries or real-time streaming. Hive/HiveQL Apache Hive is a data warehousing system that allows for SQL-like queries on a Hadoop cluster. Traditional relational databases struggle with horizontal scalability and ACID properties in large datasets, which is where Hive shines. It enables querying Hadoop data through a SQL-like query language, HiveQL, without needing complex MapReduce jobs, making it accessible to business analysts and developers. Apache Hive therefore makes it possible to query HDFS data systems using a SQL-like query language without having to write complex MapReduce processes in Java. This means that business analysts and developers can use HiveQL (Hive Query Language) to create simple queries and build evaluations based on Hadoop data architectures. Hive was originally developed by Facebook for processing large volumes of structured and semi-structured data. It is particularly useful for batch analyses and can be operated with common business intelligence tools such as Tableau or Apache Superset. The metastore is the central repository that stores metadata such as table definitions, column names, and HDFS location information. This makes it possible for Hive to manage and organize large datasets. The execution engine, on the other hand, converts HiveQL queries into tasks that Hadoop can process. Depending on the desired performance and infrastructure, you can choose different execution engines: MapReduce: The classic, slower approach. Tez: A faster alternative to MapReduce. Spark: The fastest option, which runs queries in-memory for optimal performance. To use Hive in practice, various aspects should be considered to maximize performance. For example, it is based on partitioning, so that data is not stored in a huge table, but in partitions that can be searched more quickly. For example, a company’s sales data can be partitioned by year and month: CREATE TABLE sales_partitioned ( customer_id STRING, amount DOUBLE ) PARTITIONED BY (year INT, month INT); This means that only the specific partition that is required can be accessed during a query. When creating partitions, it makes sense to create ones that are queried frequently. Buckets can also be used to ensure that joins run faster and data is distributed evenly. CREATE TABLE sales_bucketed ( customer_id STRING, amount DOUBLE ) CLUSTERED BY (customer_id) INTO 10 BUCKETS; In conclusion, Hive is a useful tool if structured queries on huge amounts of data are to be possible. It also offers an easy way to connect common BI tools, such as Tableau, with data in Hadoop. However, if the application requires many short-term read and write accesses, then Hive is not the right tool. Pig Apache Pig takes this one step further and enables the parallel processing of large amounts of data in Hadoop. Compared to Hive, it is not focused on data reporting, but on the ETL process of semi-structured and unstructured data. For these data analyses, it is not necessary to use the complex MapReduce process in Java; instead, simple processes can be written in the proprietary Pig Latin language. In addition, Pig can handle various file formats, such as JSON or XML, and perform data transformations, such as merging, filtering, or grouping data sets. The general process then looks like this: Loading the Information: The data can be pulled from different data sources, such as HDFS or HBase. Transforming the data: The data is then modified depending on the application so that you can filter, aggregate, or join it. Saving the results: Finally, the processed data can be stored in various data systems, such as HDFS, HBase, or even relational databases. Apache Pig differs from Hive in many fundamental ways. The most important are: AttributePigHiveLanguagePig Latin (script-based)HiveQL (similar to SQL)Target GroupData EngineersBusiness AnalystsData StructureSemi-structured and unstructured dataStructured DataApplicationsETL processes, data preparation, data transformationSQL-based analyses, reportingOptimizationParallel processingOptimized, analytical queriesEngine-OptionsMapReduce, Tez, SparkTez, Spark Apache Pig is a component of Hadoop that simplifies data processing through its script-based Pig Latin language and accelerates transformations by relying on parallel processing. It is particularly popular with data engineers who want to work on Hadoop without having to develop complex MapReduce programs in Java. HBase HBase is a key-value-based NoSQL database in Hadoop that stores data in a column-oriented manner. Compared to classic relational databases, it can be scaled horizontally and new servers can be added to the storage if required. The data model consists of various tables, all of which have a unique row key that can be used to uniquely identify them. This can be imagined as a primary key in a relational database. Each table in turn is made up of columns that belong to a so-called column family and must be defined when the table is created. The key-value pairs are then stored in the cells of a column. By focusing on columns instead of rows, large amounts of data can be queried particularly efficiently. This structure can also be seen when creating new data records. A unique row key is created first and the values for the individual columns can then be added to this. Put put = new Put(Bytes.toBytes(“1001”)); put.addColumn(Bytes.toBytes(“Personal”), Bytes.toBytes(“Name”), Bytes.toBytes(“Max”)); put.addColumn(Bytes.toBytes(“Bestellungen”, Bytes.toBytes(“Produkt”),Bytes.toBytes(“Laptop”)); table.put(put); The column family is named first and then the key-value pair is defined. The structure is used in the query by first defining the data set via the row key and then calling up the required column and the keys it contains. Get get = new Get(Bytes.toBytes(“1001”)); Result result = table.get(get); byte[] name = result.getValue(Bytes.toBytes(“Personal”), Bytes.toBytes(“Name”)); System.out.println(“Name: ” + Bytes.toString(name)); The structure is based on a master-worker setup. The HMaster is the higher-level control unit for HBase and manages the underlying RegionServers. It is also responsible for load distribution by centrally monitoring system performance and distributing the so-called regions to the RegionServers. If a RegionServer fails, the HMaster also ensures that the data is distributed to other RegionServers so that operations can be maintained. If the HMaster itself fails, the cluster can also have additional HMasters, which can then be retrieved from standby mode. During operation, however, a cluster only ever has one running HMaster. The RegionServers are the working units of HBase, as they store and manage the table data in the cluster. They also answer read and write requests. For this purpose, each HBase table is divided into several subsets, the so-called regions, which are then managed by the RegionServers. A RegionServer can manage several regions to manage the load between the nodes. The RegionServers work directly with clients and therefore receive the read and write requests directly. These requests end up in the so-called MemStore, whereby incoming read requests are first served from the MemStore and if the required data is no longer available there, the permanent memory in HDFS is used. As soon as the MemStore has reached a certain size, the data it contains is stored in an HFile in HDFS. The storage backend for HBase is, therefore, HDFS, which is used as permanent storage. As already described, the HFiles are used for this, which can be distributed across several nodes. The advantage of this is horizontal scalability, as the data volumes can be distributed across different machines. In addition, different copies of the data are used to ensure reliability. Finally, Apache Zookeeper serves as the superordinate instance of HBase and coordinates the distributed application. It monitors the HMaster and all RegionServers and automatically selects a new leader if an HMaster should fail. It also stores important metadata about the cluster and prevents conflicts if several clients want to access data at the same time. This enables the smooth operation of even larger clusters. HBase is, therefore, a powerful NoSQL database that is suitable for Big Data applications. Thanks to its distributed architecture, HBase remains accessible even in the event of server failures and offers a combination of RAM-supported processing in the MemStore and the permanent storage of data in HDFs. Spark Apache Spark is a further development of MapReduce and is up to 100x faster thanks to the use of in-memory computing. It has since developed into a comprehensive platform for various workloads, such as batch processing, data streaming, and even machine learning, thanks to the addition of many components. It is also compatible with a wide variety of data sources, including HDFS, Hive, and HBase. At the heart of the components is Spark Core, which offers basic functions for distributed processing: Task management: Calculations can be distributed and monitored across multiple nodes. Fault tolerance: In the event of errors in individual nodes, these can be automatically restored. In-memory computing: Data is stored in the server’s RAM to ensure fast processing and availability. The central data structures of Apache Spark are the so-called Resilient Distributed Datasets (RDDs). They enable distributed processing across different nodes and have the following properties: Resilient (fault-tolerant): Data can be restored in the event of node failures. The RDDs do not store the data themselves, but only the sequence of transformations. If a node then fails, Spark can simply re-execute the transactions to restore the RDD. Distributed: The information is distributed across multiple nodes. Immutable: Once created, RDDs cannot be changed, only recreated. Lazily evaluated (delayed execution): The operations are only executed during an action and not during the definition. Apache Spark also consists of the following components: Spark SQL provides an SQL engine for Spark and runs on datasets and DataFrames. As it works in-memory, processing is particularly fast, and it is therefore suitable for all applications where efficiency and speed play an important role. Spark streaming offers the possibility of processing continuous data streams in real-time and converting them into mini-batches. It can be used, for example, to analyze social media posts or monitor IoT data. It also supports many common streaming data sources, such as Kafka or Flume. With MLlib, Apache Spark offers an extensive library that contains a wide range of machine learning algorithms and can be applied directly to the stored data sets. This includes, for example, models for classification, regression, or even entire recommendation systems. GraphX is a powerful tool for processing and analyzing graph data. This enables efficient analyses of relationships between data points and they can be calculated simultaneously in a distributed manner. There are also special PageRank algorithms for analyzing social networks. Apache Spark is arguably one of the rising components of Hadoop, as it enables fast in-memory calculations that would previously have been unthinkable with MapReduce. Although Spark is not an exclusive component of Hadoop, as it can also use other file systems such as S3, the two systems are often used together in practice. Apache Spark is also enjoying increasing popularity due to its universal applicability and many functionalities. Oozie Apache Oozie is a workflow management and scheduling system that was developed specifically for Hadoop and plans the execution and automation of various Hadoop jobs, such as MapReduce, Spark, or Hive. The most important functionality here is that Oozie defines the dependencies between the jobs and executes them in a specific order. In addition, schedules or specific events can be defined for which the jobs are to be executed. If errors occur during execution, Oozie also has error-handling options and can restart the jobs. A workflow is defined in XML so that the workflow engine can read it and start the jobs in the correct order. If a job fails, it can simply be repeated or other steps can be initiated. Oozie also has a database backend system, such as MySQL or PostgreSQL, which is used to store status information. Presto Apache Presto offers another option for applying distributed SQL queries to large amounts of data. Compared to other Hadoop technologies, such as Hive, the queries are processed in real-time and it is therefore optimized for data warehouses running on large, distributed systems. Presto offers broad support for all relevant data sources and does not require a schema definition, so data can be queried directly from the sources. It has also been optimized to work on distributed systems and can, therefore, be used on petabyte-sized data sets. Apache Presto uses a so-called massively parallel processing (MPP) architecture, which enables particularly efficient processing in distributed systems. As soon as the user sends an SQL query via the Presto CLI or a BI front end, the coordinator analyzes the query and creates an executable query plan. The worker nodes then execute the queries and return their partial results to the coordinator, which combines them into a final result. Presto differs from the related systems in Hadoop as follows: AttributePrestoHiveSpark SQLQuery SpeedMilliseconds to secondsMinutes (batch processing)Seconds (in-memory)Processing ModelReal-time SQL queriesBatch ProcessingIn-Memory ProcessingData SourceHDFS, S3, RDBMS, NoSQL, KafkaHDFS, Hive-TablesHDFS, Hive, RDBMS, StreamsUse CaseInteractive queries, BI toolsSlow big data queriesMachine learning, streaming, SQL queries This makes Presto the best choice for fast SQL queries on a distributed big data environment like Hadoop. What are alternatives to Hadoop? Especially in the early 2010s, Hadoop was the leading technology for distributed Data Processing for a long time. However, several alternatives have since emerged that offer more advantages in certain scenarios or are simply better suited to today’s applications. Cloud-native alternatives to Hadoop Many companies have moved away from hosting their servers and on-premise systems and are instead moving their big data workloads to the cloud. There, they can benefit significantly from automatic scaling, lower maintenance costs, and better performance. In addition, many cloud providers also offer solutions that are much easier to manage than Hadoop and can, therefore, also be operated by less trained personnel. Amazon EMR (Elastic MapReduce) Amazon EMR is a managed big data service from AWS that provides Hadoop, Spark, and other distributed computing frameworks so that these clusters no longer need to be hosted on-premises. This enables companies to no longer have to actively take care of cluster maintenance and administration. In addition to Hadoop, Amazon EMR supports many other open-source frameworks, such as Spark, Hive, Presto, and HBase. This broad support means that users can simply move their existing clusters to the cloud without any major problems. For storage, Amazon uses EMR S3 as primary storage instead of HDFS. This not only makes storage cheaper as no permanent cluster is required, but it also has better availability as data is stored redundantly across multiple AWS regions. In addition, computing and storage can be scaled separately from each other and cannot be scaled exclusively via a cluster, as is the case with Hadoop. There is a specially optimized interface for the EMR File System (EMRFS) that allows direct access from Hadoop or Spark to S3. It also supports the consistency models and enables metadata caching for better performance. If necessary, HDFS can also be used, for example, if local, temporary storage is required on the cluster nodes. Another advantage of Amazon EMR over a classic Hadoop cluster is the ability to use dynamic auto-scaling to not only reduce costs but also improve performance. The cluster size and the available hardware are automatically adjusted to the CPU utilization or the job queue size so that costs are only incurred for the hardware that is needed. So-called spot indices can then only be added temporarily when they are needed. In a company, for example, it makes sense to add them at night if the data from the productive systems is to be stored in the data warehouse. During the day, on the other hand, smaller clusters are operated and costs can be saved as a result. Amazon EMR, therefore, offers several optimizations for the local use of Hadoop. The optimized storage access to S3, the dynamic cluster scaling, which increases performance and simultaneously optimizes costs, and the improved network communication between the nodes is particularly advantageous. Overall, the data can be processed faster with fewer resource requirements than with classic Hadoop clusters that run on their servers. Google BigQuery In the area of data warehousing, Google Big Query offers a fully managed and serverless data warehouse that can come up with fast SQL queries for large amounts of data. It relies on columnar data storage and uses Google Dremel technology to handle massive amounts of data more efficiently. At the same time, it can largely dispense with cluster management and infrastructure maintenance. In contrast to native Hadoop, BigQuery uses a columnar orientation and can, therefore, save immense amounts of storage space by using efficient compression methods. In addition, queries are accelerated as only the required columns need to be read rather than the entire row. This makes it possible to work much more efficiently, which is particularly noticeable with very large amounts of data. BigQuery also uses Dremel technology, which is capable of executing SQL queries in parallel hierarchies and distributing the workload across different machines. As such architectures often lose performance as soon as they have to merge the partial results again, BigQuery uses tree aggregation to combine the partial results efficiently. BigQuery is the better alternative to Hadoop, especially for applications that focus on SQL queries, such as data warehouses or business intelligence. For unstructured data, on the other hand, Hadoop may be the more suitable alternative, although the cluster architecture and the associated costs must be taken into account. Finally, BigQuery also offers a good connection to the various machine learning offerings from Google, such as Google AI or AutoML, which should be taken into account when making a selection. Snowflake If you don’t want to become dependent on the Google Cloud with BigQuery or are already pursuing a multi-cloud strategy, Snowflake can be a valid alternative for building a cloud-native data warehouse. It offers dynamic scalability by separating computing power and storage requirements so that they can be adjusted independently of each other. Compared to BigQuery, Snowflake is cloud-agnostic and can therefore be operated on common platforms such as AWS, Azure, or even in the Google Cloud. Although Snowflake also offers the option of scaling the hardware depending on requirements, there is no option for automatic scaling as with BigQuery. On the other hand, multiclusters can be created on which the data warehouse is distributed, thereby maximizing performance. On the cost side, the providers differ due to the architecture. Thanks to the complete management and automatic scaling of BigQuery, Google Cloud can calculate the costs per query and does not charge any direct costs for computing power or storage. With Snowflake, on the other hand, the choice of provider is free and so in most cases it boils down to a so-called pay-as-you-go payment model in which the provider charges the costs for storage and computing power. Overall, Snowflake offers a more flexible solution that can be hosted by various providers or even operated as a multi-cloud service. However, this requires greater knowledge of how to operate the system, as the resources have to be adapted independently. BigQuery, on the other hand, has a serverless model, which means that no infrastructure management is required. Open-source alternatives for Hadoop In addition to these complete and large cloud data platforms, several powerful open-source programs have been specifically developed as alternatives to Hadoop and specifically address its weaknesses, such as real-time data processing, performance, and complexity of administration. As we have already seen, Apache Spark is very powerful and can be used as a replacement for a Hadoop cluster, which we will not cover again. Apache Flink Apache Flink is an open-source framework that was specially developed for distributed stream processing so that data can be processed continuously. In contrast to Hadoop or Spark, which processes data in so-called micro-batches, data can be processed in near real-time with very low latency. This makes Apache Flink an alternative for applications in which information is generated continuously and needs to be reacted to in real-time, such as sensor data from machines. While Spark Streaming processes the data in so-called mini-batches and thus simulates streaming, Apache Flink offers real streaming with an event-driven model that can process data just milliseconds after it arrives. This can further minimize latency as there is no delay due to mini-batches or other waiting times. For these reasons, Flink is much better suited to high-frequency data sources, such as sensors or financial market transactions, where every second counts. Another advantage of Apache Flink is its advanced stateful processing. In many real-time applications, the context of an event plays an important role, such as the previous purchases of a customer for a product recommendation, and must therefore be saved. With Flink, this storage already takes place in the application so that long-term and stateful calculations can be carried out efficiently. This becomes particularly clear when analyzing machine data in real-time, where previous anomalies, such as too high a temperature or faulty parts, must also be included in the current report and prediction. With Hadoop or Spark, a separate database must first be accessed for this, which leads to additional latency. With Flink, on the other hand, the machine’s historical anomalies are already stored in the application so that they can be accessed directly. In conclusion, Flink is the better alternative for highly dynamic and event-based data processing. Hadoop, on the other hand, is based on batch processes and therefore cannot analyze data in real-time, as there is always a latency to wait for a completed data block. Modern data warehouses For a long time, Hadoop was the standard solution for processing large volumes of data. However, companies today also rely on modern data warehouses as an alternative, as these offer an optimized environment for structured data and thus enable faster SQL queries. In addition, there are a variety of cloud-native architectures that also offer automatic scaling, thus reducing administrative effort and saving costs. In this section, we focus on the most common data warehouse alternatives to Hadoop and explain why they may be a better choice compared to Hadoop. Amazon Redshift Amazon Redshift is a cloud-based data warehouse that was developed for structured analyses with SQL. This optimizes the processing of large relational data sets and allows fast column-based queries to be used. One of the main differences to traditional data warehouses is that data is stored in columns instead of rows, meaning that only the relevant columns need to be loaded for a query, which significantly increases efficiency. Hadoop, on the other hand, and HDFS in particular is optimized for semi-structured and unstructured data and does not natively support SQL queries. This makes Redshift ideal for OLAP analyses in which large amounts of data need to be aggregated and filtered. Another feature that increases query speed is the use of a Massive Parallel Processing (MPP) system, in which queries can be distributed across several nodes and processed in parallel. This achieves extremely high parallelization capability and processing speed. In addition, Amazon Redshift offers very good integration into Amazon’s existing systems and can be seamlessly integrated into the AWS environment without the need for open-source tools, as is the case with Hadoop. Frequently used tools are: Amazon S3 offers direct access to large amounts of data in cloud storage. AWS Glue can be used for ETL processes in which data is prepared and transformed. Amazon QuickSight is a possible tool for the visualization and analysis of data. Finally, machine learning applications can be implemented with the various AWS ML services. Amazon Redshift is a real alternative compared to Hadoop, especially for relational queries, if you are looking for a managed and scalable data warehouse solution and you already have an existing AWS cluster or want to build the architecture on top of it. It can also offer a real advantage for high query speeds and large volumes of data due to its column-based storage and massive parallel processing system. Databricks (lakehouse platform) Databricks is a cloud platform based on Apache Spark that has been specially optimized for data analysis, machine learning, and artificial intelligence. It extends the functionalities of Spark with an easy-to-understand user interface, and optimized cluster management and also offers the so-called Delta Lake, which offers data consistency, scalability, and performance compared to Hadoop-based systems. Databricks offers a fully managed environment that can be easily operated and automated using Spark clusters in the cloud. This eliminates the need for manual setup and configuration as with a Hadoop cluster. In addition, the use of Apache Spark is optimized so that batch and streaming processing can run faster and more efficiently. Finally, Databricks also includes automatic scaling, which is very valuable in the cloud environment as it can save costs and improve scalability. The classic Hadoop platforms have the problem that they do not fulfill the ACID properties and, therefore, the consistency of the data is not always guaranteed due to the distribution across different servers. With Databricks, this problem is solved with the help of the so-called Delta Lake: ACID transactions: The Delta Lake ensures that all transactions fulfill the ACID guidelines, allowing even complex pipelines to be executed completely and consistently. This ensures data integrity even in big data applications. Schema evolution: The data models can be updated dynamically so that existing workflows do not have to be adapted. Optimized storage & queries: Delta Lake uses processes such as indexing, caching, or automatic compression to make queries many times faster compared to classic Hadoop or HDFS environments. Finally, Databricks goes beyond the classic big data framework by also offering an integrated machine learning & AI platform. The most common machine learning platforms, such as TensorFlow, scikit-learn, or PyTorch, are supported so that the stored data can be processed directly. As a result, Databricks offers a simple end-to-end pipeline for machine learning applications. From data preparation to the finished model, everything can take place in Databricks and the required resources can be flexibly booked in the cloud. This makes Databricks a valid alternative to Hadoop if a data lake with ACID transactions and schema flexibility is required. It also offers additional components, such as the end-to-end solution for machine learning applications. In addition, the cluster in the cloud can not only be operated more easily and save costs by automatically adapting the hardware to the requirements, but it also offers significantly more performance than a classic Hadoop cluster due to its Spark basis. In this part, we explored the Hadoop ecosystem, highlighting key tools like Hive, Spark, and HBase, each designed to enhance Hadoop’s capabilities for various data processing tasks. From SQL-like queries with Hive to fast, in-memory processing with Spark, these components provide flexibility for big data applications. While Hadoop remains a powerful framework, alternatives such as cloud-native solutions and modern data warehouses are worth considering for different needs. This series has introduced you to Hadoop’s architecture, components, and ecosystem, giving you the foundation to build scalable, customized big data solutions. As the field continues to evolve, you’ll be equipped to choose the right tools to meet the demands of your data-driven projects.

As we have already seen with the basic components (Part 1, Part 2), the Hadoop ecosystem is constantly evolving and being optimized for new applications. As a result, various tools and technologies have developed over time that make Hadoop more powerful and even more widely applicable. As a result, it goes beyond the pure HDFS & MapReduce platform and offers, for example, SQL, as well as NoSQL queries or real-time streaming.

Hive/HiveQL

Apache Hive is a data warehousing system that allows for SQL-like queries on a Hadoop cluster. Traditional relational databases struggle with horizontal scalability and ACID properties in large datasets, which is where Hive shines. It enables querying Hadoop data through a SQL-like query language, HiveQL, without needing complex MapReduce jobs, making it accessible to business analysts and developers.

Apache Hive therefore makes it possible to query HDFS data systems using a SQL-like query language without having to write complex MapReduce processes in Java. This means that business analysts and developers can use HiveQL (Hive Query Language) to create simple queries and build evaluations based on Hadoop data architectures.

Hive was originally developed by Facebook for processing large volumes of structured and semi-structured data. It is particularly useful for batch analyses and can be operated with common business intelligence tools such as Tableau or Apache Superset.

The metastore is the central repository that stores metadata such as table definitions, column names, and HDFS location information. This makes it possible for Hive to manage and organize large datasets. The execution engine, on the other hand, converts HiveQL queries into tasks that Hadoop can process. Depending on the desired performance and infrastructure, you can choose different execution engines:

  • MapReduce: The classic, slower approach.
  • Tez: A faster alternative to MapReduce.
  • Spark: The fastest option, which runs queries in-memory for optimal performance.

To use Hive in practice, various aspects should be considered to maximize performance. For example, it is based on partitioning, so that data is not stored in a huge table, but in partitions that can be searched more quickly. For example, a company’s sales data can be partitioned by year and month:

CREATE TABLE sales_partitioned (
    customer_id STRING,
    amount DOUBLE
) PARTITIONED BY (year INT, month INT);

This means that only the specific partition that is required can be accessed during a query. When creating partitions, it makes sense to create ones that are queried frequently. Buckets can also be used to ensure that joins run faster and data is distributed evenly.

CREATE TABLE sales_bucketed (
    customer_id STRING,
    amount DOUBLE
) CLUSTERED BY (customer_id) INTO 10 BUCKETS;

In conclusion, Hive is a useful tool if structured queries on huge amounts of data are to be possible. It also offers an easy way to connect common BI tools, such as Tableau, with data in Hadoop. However, if the application requires many short-term read and write accesses, then Hive is not the right tool.

Pig

Apache Pig takes this one step further and enables the parallel processing of large amounts of data in Hadoop. Compared to Hive, it is not focused on data reporting, but on the ETL process of semi-structured and unstructured data. For these data analyses, it is not necessary to use the complex MapReduce process in Java; instead, simple processes can be written in the proprietary Pig Latin language.

In addition, Pig can handle various file formats, such as JSON or XML, and perform data transformations, such as merging, filtering, or grouping data sets. The general process then looks like this:

  • Loading the Information: The data can be pulled from different data sources, such as HDFS or HBase.
  • Transforming the data: The data is then modified depending on the application so that you can filter, aggregate, or join it.
  • Saving the results: Finally, the processed data can be stored in various data systems, such as HDFS, HBase, or even relational databases.

Apache Pig differs from Hive in many fundamental ways. The most important are:

Attribute Pig Hive
Language Pig Latin (script-based) HiveQL (similar to SQL)
Target Group Data Engineers Business Analysts
Data Structure Semi-structured and unstructured data Structured Data
Applications ETL processes, data preparation, data transformation SQL-based analyses, reporting
Optimization Parallel processing Optimized, analytical queries
Engine-Options MapReduce, Tez, Spark Tez, Spark

Apache Pig is a component of Hadoop that simplifies data processing through its script-based Pig Latin language and accelerates transformations by relying on parallel processing. It is particularly popular with data engineers who want to work on Hadoop without having to develop complex MapReduce programs in Java.

HBase

HBase is a key-value-based NoSQL database in Hadoop that stores data in a column-oriented manner. Compared to classic relational databases, it can be scaled horizontally and new servers can be added to the storage if required. The data model consists of various tables, all of which have a unique row key that can be used to uniquely identify them. This can be imagined as a primary key in a relational database.

Each table in turn is made up of columns that belong to a so-called column family and must be defined when the table is created. The key-value pairs are then stored in the cells of a column. By focusing on columns instead of rows, large amounts of data can be queried particularly efficiently.

This structure can also be seen when creating new data records. A unique row key is created first and the values for the individual columns can then be added to this.

Put put = new Put(Bytes.toBytes("1001"));
put.addColumn(Bytes.toBytes("Personal"), Bytes.toBytes("Name"), Bytes.toBytes("Max"));
put.addColumn(Bytes.toBytes("Bestellungen", Bytes.toBytes("Produkt"),Bytes.toBytes("Laptop"));
table.put(put);

The column family is named first and then the key-value pair is defined. The structure is used in the query by first defining the data set via the row key and then calling up the required column and the keys it contains.

Get get = new Get(Bytes.toBytes("1001"));
Result result = table.get(get);
byte[] name = result.getValue(Bytes.toBytes("Personal"), Bytes.toBytes("Name"));
System.out.println("Name: " + Bytes.toString(name));

The structure is based on a master-worker setup. The HMaster is the higher-level control unit for HBase and manages the underlying RegionServers. It is also responsible for load distribution by centrally monitoring system performance and distributing the so-called regions to the RegionServers. If a RegionServer fails, the HMaster also ensures that the data is distributed to other RegionServers so that operations can be maintained. If the HMaster itself fails, the cluster can also have additional HMasters, which can then be retrieved from standby mode. During operation, however, a cluster only ever has one running HMaster.

The RegionServers are the working units of HBase, as they store and manage the table data in the cluster. They also answer read and write requests. For this purpose, each HBase table is divided into several subsets, the so-called regions, which are then managed by the RegionServers. A RegionServer can manage several regions to manage the load between the nodes.

The RegionServers work directly with clients and therefore receive the read and write requests directly. These requests end up in the so-called MemStore, whereby incoming read requests are first served from the MemStore and if the required data is no longer available there, the permanent memory in HDFS is used. As soon as the MemStore has reached a certain size, the data it contains is stored in an HFile in HDFS.

The storage backend for HBase is, therefore, HDFS, which is used as permanent storage. As already described, the HFiles are used for this, which can be distributed across several nodes. The advantage of this is horizontal scalability, as the data volumes can be distributed across different machines. In addition, different copies of the data are used to ensure reliability.

Finally, Apache Zookeeper serves as the superordinate instance of HBase and coordinates the distributed application. It monitors the HMaster and all RegionServers and automatically selects a new leader if an HMaster should fail. It also stores important metadata about the cluster and prevents conflicts if several clients want to access data at the same time. This enables the smooth operation of even larger clusters.

HBase is, therefore, a powerful NoSQL database that is suitable for Big Data applications. Thanks to its distributed architecture, HBase remains accessible even in the event of server failures and offers a combination of RAM-supported processing in the MemStore and the permanent storage of data in HDFs.

Spark

Apache Spark is a further development of MapReduce and is up to 100x faster thanks to the use of in-memory computing. It has since developed into a comprehensive platform for various workloads, such as batch processing, data streaming, and even machine learning, thanks to the addition of many components. It is also compatible with a wide variety of data sources, including HDFS, Hive, and HBase.

At the heart of the components is Spark Core, which offers basic functions for distributed processing:

  • Task management: Calculations can be distributed and monitored across multiple nodes.
  • Fault tolerance: In the event of errors in individual nodes, these can be automatically restored.
  • In-memory computing: Data is stored in the server’s RAM to ensure fast processing and availability.

The central data structures of Apache Spark are the so-called Resilient Distributed Datasets (RDDs). They enable distributed processing across different nodes and have the following properties:

  • Resilient (fault-tolerant): Data can be restored in the event of node failures. The RDDs do not store the data themselves, but only the sequence of transformations. If a node then fails, Spark can simply re-execute the transactions to restore the RDD.
  • Distributed: The information is distributed across multiple nodes.
  • Immutable: Once created, RDDs cannot be changed, only recreated.
  • Lazily evaluated (delayed execution): The operations are only executed during an action and not during the definition.

Apache Spark also consists of the following components:

  • Spark SQL provides an SQL engine for Spark and runs on datasets and DataFrames. As it works in-memory, processing is particularly fast, and it is therefore suitable for all applications where efficiency and speed play an important role.
  • Spark streaming offers the possibility of processing continuous data streams in real-time and converting them into mini-batches. It can be used, for example, to analyze social media posts or monitor IoT data. It also supports many common streaming data sources, such as Kafka or Flume.
  • With MLlib, Apache Spark offers an extensive library that contains a wide range of machine learning algorithms and can be applied directly to the stored data sets. This includes, for example, models for classification, regression, or even entire recommendation systems.
  • GraphX is a powerful tool for processing and analyzing graph data. This enables efficient analyses of relationships between data points and they can be calculated simultaneously in a distributed manner. There are also special PageRank algorithms for analyzing social networks.

Apache Spark is arguably one of the rising components of Hadoop, as it enables fast in-memory calculations that would previously have been unthinkable with MapReduce. Although Spark is not an exclusive component of Hadoop, as it can also use other file systems such as S3, the two systems are often used together in practice. Apache Spark is also enjoying increasing popularity due to its universal applicability and many functionalities.

Oozie

Apache Oozie is a workflow management and scheduling system that was developed specifically for Hadoop and plans the execution and automation of various Hadoop jobs, such as MapReduce, Spark, or Hive. The most important functionality here is that Oozie defines the dependencies between the jobs and executes them in a specific order. In addition, schedules or specific events can be defined for which the jobs are to be executed. If errors occur during execution, Oozie also has error-handling options and can restart the jobs.

A workflow is defined in XML so that the workflow engine can read it and start the jobs in the correct order. If a job fails, it can simply be repeated or other steps can be initiated. Oozie also has a database backend system, such as MySQL or PostgreSQL, which is used to store status information.

Presto

Apache Presto offers another option for applying distributed SQL queries to large amounts of data. Compared to other Hadoop technologies, such as Hive, the queries are processed in real-time and it is therefore optimized for data warehouses running on large, distributed systems. Presto offers broad support for all relevant data sources and does not require a schema definition, so data can be queried directly from the sources. It has also been optimized to work on distributed systems and can, therefore, be used on petabyte-sized data sets.

Apache Presto uses a so-called massively parallel processing (MPP) architecture, which enables particularly efficient processing in distributed systems. As soon as the user sends an SQL query via the Presto CLI or a BI front end, the coordinator analyzes the query and creates an executable query plan. The worker nodes then execute the queries and return their partial results to the coordinator, which combines them into a final result.

Presto differs from the related systems in Hadoop as follows:

Attribute Presto Hive Spark SQL
Query Speed Milliseconds to seconds Minutes (batch processing) Seconds (in-memory)
Processing Model Real-time SQL queries Batch Processing In-Memory Processing
Data Source HDFS, S3, RDBMS, NoSQL, Kafka HDFS, Hive-Tables HDFS, Hive, RDBMS, Streams
Use Case Interactive queries, BI tools Slow big data queries Machine learning, streaming, SQL queries

This makes Presto the best choice for fast SQL queries on a distributed big data environment like Hadoop.

What are alternatives to Hadoop?

Especially in the early 2010s, Hadoop was the leading technology for distributed Data Processing for a long time. However, several alternatives have since emerged that offer more advantages in certain scenarios or are simply better suited to today’s applications.

Cloud-native alternatives to Hadoop

Many companies have moved away from hosting their servers and on-premise systems and are instead moving their big data workloads to the cloud. There, they can benefit significantly from automatic scaling, lower maintenance costs, and better performance. In addition, many cloud providers also offer solutions that are much easier to manage than Hadoop and can, therefore, also be operated by less trained personnel.

Amazon EMR (Elastic MapReduce)

Amazon EMR is a managed big data service from AWS that provides Hadoop, Spark, and other distributed computing frameworks so that these clusters no longer need to be hosted on-premises. This enables companies to no longer have to actively take care of cluster maintenance and administration. In addition to Hadoop, Amazon EMR supports many other open-source frameworks, such as Spark, Hive, Presto, and HBase. This broad support means that users can simply move their existing clusters to the cloud without any major problems.

For storage, Amazon uses EMR S3 as primary storage instead of HDFS. This not only makes storage cheaper as no permanent cluster is required, but it also has better availability as data is stored redundantly across multiple AWS regions. In addition, computing and storage can be scaled separately from each other and cannot be scaled exclusively via a cluster, as is the case with Hadoop.

There is a specially optimized interface for the EMR File System (EMRFS) that allows direct access from Hadoop or Spark to S3. It also supports the consistency models and enables metadata caching for better performance. If necessary, HDFS can also be used, for example, if local, temporary storage is required on the cluster nodes.

Another advantage of Amazon EMR over a classic Hadoop cluster is the ability to use dynamic auto-scaling to not only reduce costs but also improve performance. The cluster size and the available hardware are automatically adjusted to the CPU utilization or the job queue size so that costs are only incurred for the hardware that is needed.

So-called spot indices can then only be added temporarily when they are needed. In a company, for example, it makes sense to add them at night if the data from the productive systems is to be stored in the data warehouse. During the day, on the other hand, smaller clusters are operated and costs can be saved as a result.

Amazon EMR, therefore, offers several optimizations for the local use of Hadoop. The optimized storage access to S3, the dynamic cluster scaling, which increases performance and simultaneously optimizes costs, and the improved network communication between the nodes is particularly advantageous. Overall, the data can be processed faster with fewer resource requirements than with classic Hadoop clusters that run on their servers.

Google BigQuery

In the area of data warehousing, Google Big Query offers a fully managed and serverless data warehouse that can come up with fast SQL queries for large amounts of data. It relies on columnar data storage and uses Google Dremel technology to handle massive amounts of data more efficiently. At the same time, it can largely dispense with cluster management and infrastructure maintenance.

In contrast to native Hadoop, BigQuery uses a columnar orientation and can, therefore, save immense amounts of storage space by using efficient compression methods. In addition, queries are accelerated as only the required columns need to be read rather than the entire row. This makes it possible to work much more efficiently, which is particularly noticeable with very large amounts of data.

BigQuery also uses Dremel technology, which is capable of executing SQL queries in parallel hierarchies and distributing the workload across different machines. As such architectures often lose performance as soon as they have to merge the partial results again, BigQuery uses tree aggregation to combine the partial results efficiently.

BigQuery is the better alternative to Hadoop, especially for applications that focus on SQL queries, such as data warehouses or business intelligence. For unstructured data, on the other hand, Hadoop may be the more suitable alternative, although the cluster architecture and the associated costs must be taken into account. Finally, BigQuery also offers a good connection to the various machine learning offerings from Google, such as Google AI or AutoML, which should be taken into account when making a selection.

Snowflake

If you don’t want to become dependent on the Google Cloud with BigQuery or are already pursuing a multi-cloud strategy, Snowflake can be a valid alternative for building a cloud-native data warehouse. It offers dynamic scalability by separating computing power and storage requirements so that they can be adjusted independently of each other.

Compared to BigQuery, Snowflake is cloud-agnostic and can therefore be operated on common platforms such as AWS, Azure, or even in the Google Cloud. Although Snowflake also offers the option of scaling the hardware depending on requirements, there is no option for automatic scaling as with BigQuery. On the other hand, multiclusters can be created on which the data warehouse is distributed, thereby maximizing performance.

On the cost side, the providers differ due to the architecture. Thanks to the complete management and automatic scaling of BigQuery, Google Cloud can calculate the costs per query and does not charge any direct costs for computing power or storage. With Snowflake, on the other hand, the choice of provider is free and so in most cases it boils down to a so-called pay-as-you-go payment model in which the provider charges the costs for storage and computing power.

Overall, Snowflake offers a more flexible solution that can be hosted by various providers or even operated as a multi-cloud service. However, this requires greater knowledge of how to operate the system, as the resources have to be adapted independently. BigQuery, on the other hand, has a serverless model, which means that no infrastructure management is required.

Open-source alternatives for Hadoop

In addition to these complete and large cloud data platforms, several powerful open-source programs have been specifically developed as alternatives to Hadoop and specifically address its weaknesses, such as real-time data processing, performance, and complexity of administration. As we have already seen, Apache Spark is very powerful and can be used as a replacement for a Hadoop cluster, which we will not cover again.

Apache Flink

Apache Flink is an open-source framework that was specially developed for distributed stream processing so that data can be processed continuously. In contrast to Hadoop or Spark, which processes data in so-called micro-batches, data can be processed in near real-time with very low latency. This makes Apache Flink an alternative for applications in which information is generated continuously and needs to be reacted to in real-time, such as sensor data from machines.

While Spark Streaming processes the data in so-called mini-batches and thus simulates streaming, Apache Flink offers real streaming with an event-driven model that can process data just milliseconds after it arrives. This can further minimize latency as there is no delay due to mini-batches or other waiting times. For these reasons, Flink is much better suited to high-frequency data sources, such as sensors or financial market transactions, where every second counts.

Another advantage of Apache Flink is its advanced stateful processing. In many real-time applications, the context of an event plays an important role, such as the previous purchases of a customer for a product recommendation, and must therefore be saved. With Flink, this storage already takes place in the application so that long-term and stateful calculations can be carried out efficiently.

This becomes particularly clear when analyzing machine data in real-time, where previous anomalies, such as too high a temperature or faulty parts, must also be included in the current report and prediction. With Hadoop or Spark, a separate database must first be accessed for this, which leads to additional latency. With Flink, on the other hand, the machine’s historical anomalies are already stored in the application so that they can be accessed directly.

In conclusion, Flink is the better alternative for highly dynamic and event-based data processing. Hadoop, on the other hand, is based on batch processes and therefore cannot analyze data in real-time, as there is always a latency to wait for a completed data block.

Modern data warehouses

For a long time, Hadoop was the standard solution for processing large volumes of data. However, companies today also rely on modern data warehouses as an alternative, as these offer an optimized environment for structured data and thus enable faster SQL queries. In addition, there are a variety of cloud-native architectures that also offer automatic scaling, thus reducing administrative effort and saving costs.

In this section, we focus on the most common data warehouse alternatives to Hadoop and explain why they may be a better choice compared to Hadoop.

Amazon Redshift

Amazon Redshift is a cloud-based data warehouse that was developed for structured analyses with SQL. This optimizes the processing of large relational data sets and allows fast column-based queries to be used.

One of the main differences to traditional data warehouses is that data is stored in columns instead of rows, meaning that only the relevant columns need to be loaded for a query, which significantly increases efficiency. Hadoop, on the other hand, and HDFS in particular is optimized for semi-structured and unstructured data and does not natively support SQL queries. This makes Redshift ideal for OLAP analyses in which large amounts of data need to be aggregated and filtered.

Another feature that increases query speed is the use of a Massive Parallel Processing (MPP) system, in which queries can be distributed across several nodes and processed in parallel. This achieves extremely high parallelization capability and processing speed.

In addition, Amazon Redshift offers very good integration into Amazon’s existing systems and can be seamlessly integrated into the AWS environment without the need for open-source tools, as is the case with Hadoop. Frequently used tools are:

  • Amazon S3 offers direct access to large amounts of data in cloud storage.
  • AWS Glue can be used for ETL processes in which data is prepared and transformed.
  • Amazon QuickSight is a possible tool for the visualization and analysis of data.
  • Finally, machine learning applications can be implemented with the various AWS ML services.

Amazon Redshift is a real alternative compared to Hadoop, especially for relational queries, if you are looking for a managed and scalable data warehouse solution and you already have an existing AWS cluster or want to build the architecture on top of it. It can also offer a real advantage for high query speeds and large volumes of data due to its column-based storage and massive parallel processing system.

Databricks (lakehouse platform)

Databricks is a cloud platform based on Apache Spark that has been specially optimized for data analysis, machine learning, and artificial intelligence. It extends the functionalities of Spark with an easy-to-understand user interface, and optimized cluster management and also offers the so-called Delta Lake, which offers data consistency, scalability, and performance compared to Hadoop-based systems.

Databricks offers a fully managed environment that can be easily operated and automated using Spark clusters in the cloud. This eliminates the need for manual setup and configuration as with a Hadoop cluster. In addition, the use of Apache Spark is optimized so that batch and streaming processing can run faster and more efficiently. Finally, Databricks also includes automatic scaling, which is very valuable in the cloud environment as it can save costs and improve scalability.

The classic Hadoop platforms have the problem that they do not fulfill the ACID properties and, therefore, the consistency of the data is not always guaranteed due to the distribution across different servers. With Databricks, this problem is solved with the help of the so-called Delta Lake:

  • ACID transactions: The Delta Lake ensures that all transactions fulfill the ACID guidelines, allowing even complex pipelines to be executed completely and consistently. This ensures data integrity even in big data applications.
  • Schema evolution: The data models can be updated dynamically so that existing workflows do not have to be adapted.
  • Optimized storage & queries: Delta Lake uses processes such as indexing, caching, or automatic compression to make queries many times faster compared to classic Hadoop or HDFS environments.

Finally, Databricks goes beyond the classic big data framework by also offering an integrated machine learning & AI platform. The most common machine learning platforms, such as TensorFlow, scikit-learn, or PyTorch, are supported so that the stored data can be processed directly. As a result, Databricks offers a simple end-to-end pipeline for machine learning applications. From data preparation to the finished model, everything can take place in Databricks and the required resources can be flexibly booked in the cloud.

This makes Databricks a valid alternative to Hadoop if a data lake with ACID transactions and schema flexibility is required. It also offers additional components, such as the end-to-end solution for machine learning applications. In addition, the cluster in the cloud can not only be operated more easily and save costs by automatically adapting the hardware to the requirements, but it also offers significantly more performance than a classic Hadoop cluster due to its Spark basis.


In this part, we explored the Hadoop ecosystem, highlighting key tools like Hive, Spark, and HBase, each designed to enhance Hadoop’s capabilities for various data processing tasks. From SQL-like queries with Hive to fast, in-memory processing with Spark, these components provide flexibility for big data applications. While Hadoop remains a powerful framework, alternatives such as cloud-native solutions and modern data warehouses are worth considering for different needs.

This series has introduced you to Hadoop’s architecture, components, and ecosystem, giving you the foundation to build scalable, customized big data solutions. As the field continues to evolve, you’ll be equipped to choose the right tools to meet the demands of your data-driven projects.

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EPA to end environmental justice programs, monitoring tools

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Intel under Tan: What enterprise IT buyers need to know

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Qatar Supplies Syria With Natural Gas in Latest Post-Assad Boost

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Energy Bosses Shrug Off DeepSeek to Focus on Powering AI Boom

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IBM laying foundation for mainframe as ultimate AI server

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VergeIO enhances VergeFabric network virtualization offering

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Podcast: On the Frontier of Modular Edge AI Data Centers with Flexnode’s Andrew Lindsey

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Last Energy to Deploy 30 Microreactors in Texas for Data Centers

As the demand for data center power surges in Texas, nuclear startup Last Energy has now announced plans to build 30 microreactors in the state’s Haskell County near the Dallas-Fort Worth Metroplex. The reactors will serve a growing customer base of data center operators in the region looking for reliable, carbon-free energy. The plan marks Last Energy’s largest project to date and a significant step in advancing modular nuclear power as a viable solution for high-density computing infrastructure. Meeting the Looming Power Demands of Texas Data Centers Texas is already home to over 340 data centers, with significant expansion underway. Google is increasing its data center footprint in Dallas, while OpenAI’s Stargate has announced plans for a new facility in Abilene, just an hour south of Last Energy’s planned site. The company notes the Dallas-Fort Worth metro area alone is projected to require an additional 43 gigawatts of power in the coming years, far surpassing current grid capacity. To help remediate, Last Energy has secured a 200+ acre site in Haskell County, approximately three and a half hours west of Dallas. The company has also filed for a grid connection with ERCOT, with plans to deliver power via a mix of private wire and grid transmission. Additionally, Last Energy has begun pre-application engagement with the U.S. Nuclear Regulatory Commission (NRC) for an Early Site Permit, a key step in securing regulatory approval. According to Last Energy CEO Bret Kugelmass, the company’s modular approach is designed to bring nuclear energy online faster than traditional projects. “Nuclear power is the most effective way to meet Texas’ growing energy demand, but it needs to be deployed faster and at scale,” Kugelmass said. “Our microreactors are designed to be plug-and-play, enabling data center operators to bypass the constraints of an overloaded grid.” Scaling Nuclear for

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Data Center Jobs: Engineering and Technician Jobs Available in Major Markets

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting.  Data Center Facility Engineer (Night Shift Available) Ashburn, VAThis position is also available in: Tacoma, WA (Nights), Days/Nights: Needham, MA and New York City, NY. This opportunity is working directly with a leading mission-critical data center developer / wholesaler / colo provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations facilities supporting enterprise clients and hyperscale companies. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OHThis traveling position is also available in: Somerset, NJ; Boydton, VA; Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Des Moines, IA; San Jose, CA; Portland, OR; St Louis, MO; Phoenix, AZ;  Dallas, TX;  Chicago, IL; or Toronto, ON. *** ALSO looking for a LEAD EE and ME CxA agents.*** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Switchgear Field Service Technician – Critical Facilities Nationwide TravelThis position is also available in: Charlotte, NC; Atlanta, GA; Dallas,

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Amid Shifting Regional Data Center Policies, Iron Mountain and DC Blox Both Expand in Virginia’s Henrico County

The dynamic landscape of data center developments in Maryland and Virginia exemplify the intricate balance between fostering technological growth and addressing community and environmental concerns. Data center developers in this region find themselves both in the crosshairs of groups worried about the environment and other groups looking to drive economic growth. In some cases, the groups are different components of the same organizations, such as local governments. For data center development, meeting the needs of these competing interests often means walking a none-too-stable tightrope. Rapid Government Action Encourages Growth In May 2024, Maryland demonstrated its commitment to attracting data center investments by enacting the Critical Infrastructure Streamlining Act. This legislation provides a clear framework for the use of emergency backup power generation, addressing previous regulatory challenges that a few months earlier had hindered projects like Aligned Data Centers’ proposed 264-megawatt campus in Frederick County, causing Aligned to pull out of the project. However, just days after the Act was signed by the governor, Aligned reiterated its plans to move forward with development in Maryland.  With the Quantum Loop and the related data center development making Frederick County a focal point for a balanced approach, the industry is paying careful attention to the pace of development and the relations between developers, communities and the government. In September of 2024, Frederick County Executive Jessica Fitzwater revealed draft legislation that would potentially restrict where in the county data centers could be built. The legislation was based on information found in the Frederick County Data Centers Workgroup’s final report. Those bills would update existing regulations and create a floating zone for Critical Digital Infrastructure and place specific requirements on siting data centers. Statewide, a cautious approach to environmental and community impacts statewide has been deemed important. In January 2025, legislators introduced SB116,  a bill

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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