Mastering Hadoop, Part 1: Installation, Configuration, and Modern Big Data Strategies

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Mastering Hadoop, Part 1: Installation, Configuration, and Modern Big Data Strategies

Nowadays, a large amount of data is collected on the internet, which is why companies are faced with the challenge of being able to store, process, and analyze these volumes efficiently. Hadoop is an open-source framework from the Apache Software Foundation and has become one of the leading Big Data management technologies in recent years. The system enables the distributed storage and processing of data across multiple servers. As a result, it offers a scalable solution for a wide range of applications from data analysis to machine learning. This article provides a comprehensive overview of Hadoop and its components. We also examine the underlying architecture and provide practical tips for getting started with it. Before we can start with it, we need to mention that the whole topic of Hadoop is huge and even though this article is already long, it is not even close to going into too much detail on all topics. This is why we split it into three parts: To let you decide for yourself how deep you want to dive into it: Part 1: Hadoop 101: What it is, why it matters, and who should care This part is for everyone interested in Big Data and Data Science that wants to get to know this classic tool and also understand the downsides of it. Part 2: Getting Hands-On: Setting up and scaling Hadoop All readers that weren’t scared off by the disadvantages of Hadoop and the size of the ecosystem, can use this part to get a guideline on how they can start with their first local cluster to learn the basics on how to operate it. Part 3: Hadoop ecosystem: Get the most out of your cluster In this section, we go under the hood and explain the core components and how they can be further advanced to meet your requirements. Part 1: Hadoop 101: What it is, why it matters, and who should care Hadoop is an open-source framework for the distributed storage and processing of large amounts of data. It was originally developed by Doug Cutting and Mike Cafarella and started as a search engine optimization project under the name Nutch. It was only later renamed Hadoop by its founder Cutting, based on the name of his son’s toy elephant. This is where the yellow elephant in today’s logo comes from. The original concept was based on two Google papers on distributed file systems and the MapReduce mechanism and initially comprised around 11,000 lines of code. Other methods, such as the YARN resource manager, were only added in 2012. Today, the ecosystem comprises a large number of components that go far beyond pure file storage. Hadoop differs fundamentally from traditional relational databases (RDBMS): AttributeHadoopRDBMSData StructureUnstructured, semi-structured, and unstructured dataStructured DataProcessingBatch processing or partial real-time processingTransaction-based with SQLScalabilityHorizontal scaling across multiple serversVertical scaling through stronger serversFlexibilitySupports many data formatsStrict schemes must be adhered toCostsOpen source with affordable hardwareMostly open source, but with powerful, expensive servers Which applications use Hadoop? Hadoop is an important big data framework that has established itself in many companies and applications in recent years. In general, it can be used primarily for the storage of large and unstructured data volumes and, thanks to its distributed architecture, is particularly suitable for data-intensive applications that would not be manageable with traditional databases. Typical use cases for Hadoop include: Big data analysis: Hadoop enables companies to centrally collect and store large amounts of data from different systems. This data can then be processed for further analysis and made available to users in reports. Both structured data, such as financial transactions or sensor data, and unstructured data, such as social media comments or website usage data, can be stored in Hadoop. Log analysis & IT monitoring: In modern IT infrastructure, a wide variety of systems generate data in the form of logs that provide information about the status or log certain events. This information needs to be stored and reacted to in real-time, for example, to prevent failures if the memory is full or the program is not working as expected. Hadoop can take on the task of data storage by distributing the data across several nodes and processing it in parallel, while also analyzing the information in batches. Machine learning & AI: Hadoop provides the basis for many machine learning and AI models by managing the data sets for large models. In text or image processing in particular, the model architectures require a lot of training data that takes up large amounts of memory. With the help of Hadoop, this storage can be managed and operated efficiently so that the focus can be on the architecture and training of the AI algorithms. ETL processes: ETL processes are essential in companies to prepare the data so that it can be processed further or used for analysis. To do this, it must be collected from a wide variety of systems, then transformed and finally stored in a data lake or data warehouse. Hadoop can provide central support here by offering a good connection to different data sources and allowing Data Processing to be parallelized across multiple servers. In addition, cost efficiency can be increased, especially in comparison to classic ETL approaches with data warehouses. The list of well-known companies that use Hadoop daily and have made it an integral part of their architecture is very long. Facebook, for example, uses Hadoop to process several petabytes of user data every day for advertisements, feed optimization, and machine learning. Twitter, on the other hand, uses Hadoop for real-time trend analysis or to detect spam, which should be flagged accordingly. Finally, Yahoo has one of the world’s largest Hadoop installations with over 40,000 nodes, which was set up to analyze search and advertising data. What are the advantages and disadvantages of Hadoop? Hadoop has become a powerful and popular big data framework used by many companies, especially in the 2010s, due to its ability to process large amounts of data in a distributed manner. In general, the following advantages arise when using Hadoop: Scalability: The cluster can easily be scaled horizontally by adding new nodes that take on additional tasks for a job. This also makes it possible to process data volumes that exceed the capacity of a single computer. Cost efficiency: This horizontal scalability also makes Hadoop very cost-efficient, as more low-cost computers can be added for better performance instead of equipping a server with expensive hardware and scaling vertically. In addition, Hadoop is open-source software and can therefore be used free of charge. Flexibility: Hadoop can process both unstructured data and structured data, offering the flexibility to be used for a wide variety of applications. It offers additional flexibility by providing a large library of components that further extend the existing functionalities. Fault tolerance: By replicating the data across different servers, the system can still function in the event of most hardware failures, as it simply falls back on another replication. This also results in high availability of the entire system. These disadvantages should also be taken into account. Complexity: Due to the strong networking of the cluster and the individual servers in it, the administration of the system is rather complex, and a certain amount of training is required to set up and operate a Hadoop cluster correctly. However, this point can be avoided by using a cloud connection and the automatic scaling it contains. Latency: Hadoop uses batch processing to handle the data and thus establishes latency times, as the data is not processed in real-time, but only when enough data is available for a batch. Hadoop tries to avoid this with the help of mini-batches, but this still means latency. Data management: Additional components are required for data management, such as data quality control or tracking the data sequence. Hadoop does not include any direct tools for data management. Hadoop is a powerful tool for processing big data. Above all, scalability, cost efficiency, and flexibility are decisive advantages that have contributed to the widespread use of Hadoop. However, there are also some disadvantages, such as the latency caused by batch processing. Does Hadoop have a future? Hadoop has long been the leading technology for distributed big data processing, but new systems have also emerged and become increasingly relevant in recent years. One of the biggest trends is that most companies are turning to fully managed cloud data platforms that can run Hadoop-like workloads without the need for a dedicated cluster. This also makes them more cost-efficient, as only the hardware that is needed has to be paid for. In addition, Apache Spark in particular has established itself as a faster alternative to MapReduce and is therefore outperforming the classic Hadoop setup. It is also interesting because it offers an almost complete solution for AI workloads thanks to its various functionalities, such as Apache Streaming or the machine learning library. Although Hadoop remains a relevant big data framework, it is slowly losing importance these days. Even though many established companies continue to rely on the clusters that were set up some time ago, companies that are now starting with big data are using cloud solutions or specialized analysis software directly. Accordingly, the Hadoop platform is also evolving and offers new solutions that adapt to this zeitgeist. Who should still learn Hadoop? With the rise of cloud-native data platforms and modern distributed computing frameworks, you might be wondering: Is Hadoop still worth learning? The answer depends on your role, industry, and the scale of data you work with. While Hadoop is no longer the default choice for big data processing, it remains highly relevant in many enterprise environments. Hadoop could be still relevant for you if at least one of the following is true for you: Your company still has a Hadoop-based data lake. The data you are storing is confidential and needs to be hosted on-premises. You work with ETL processes, and data ingestion at scale. Your goal is to optimize batch-processing jobs in a distributed environment. You need to work with tools like Hive, HBase, or Apache Spark on Hadoop. You want to optimize cost-efficient data storage and processing solutions. Hadoop is definitely not necessary for every data professional. If you’re working primarily with cloud-native analytics tools, serverless architectures, or lightweight data-wrangling tasks, spending time on Hadoop may not be the best investment. You can skip Hadoop if: Your work is focused on SQL-based analytics with cloud-native solutions (e.g., BigQuery, Snowflake, Redshift). You primarily handle small to mid-sized datasets in Python or Pandas. Your company has already migrated away from Hadoop to fully cloud-based architectures. Hadoop is no longer the cutting edge technology that it once was, but it still has importance in different applications and companies with existing data lakes, large-scale ETL processes, or on-premises infrastructure. In the following part, we will finally be more practical and show how an easy cluster can be set up to build your big data framework with Hadoop.

This article provides a comprehensive overview of Hadoop and its components. We also examine the underlying architecture and provide practical tips for getting started with it.

Before we can start with it, we need to mention that the whole topic of Hadoop is huge and even though this article is already long, it is not even close to going into too much detail on all topics. This is why we split it into three parts: To let you decide for yourself how deep you want to dive into it:

Part 1: Hadoop 101: What it is, why it matters, and who should care

This part is for everyone interested in Big Data and Data Science that wants to get to know this classic tool and also understand the downsides of it.

Part 2: Getting Hands-On: Setting up and scaling Hadoop

All readers that weren’t scared off by the disadvantages of Hadoop and the size of the ecosystem, can use this part to get a guideline on how they can start with their first local cluster to learn the basics on how to operate it.

Part 3: Hadoop ecosystem: Get the most out of your cluster

In this section, we go under the hood and explain the core components and how they can be further advanced to meet your requirements.

Part 1: Hadoop 101: What it is, why it matters, and who should care

Hadoop is an open-source framework for the distributed storage and processing of large amounts of data. It was originally developed by Doug Cutting and Mike Cafarella and started as a search engine optimization project under the name Nutch. It was only later renamed Hadoop by its founder Cutting, based on the name of his son’s toy elephant. This is where the yellow elephant in today’s logo comes from.

The original concept was based on two Google papers on distributed file systems and the MapReduce mechanism and initially comprised around 11,000 lines of code. Other methods, such as the YARN resource manager, were only added in 2012. Today, the ecosystem comprises a large number of components that go far beyond pure file storage.

Hadoop differs fundamentally from traditional relational databases (RDBMS):

Attribute	Hadoop	RDBMS
Data Structure	Unstructured, semi-structured, and unstructured data	Structured Data
Processing	Batch processing or partial real-time processing	Transaction-based with SQL
Scalability	Horizontal scaling across multiple servers	Vertical scaling through stronger servers
Flexibility	Supports many data formats	Strict schemes must be adhered to
Costs	Open source with affordable hardware	Mostly open source, but with powerful, expensive servers

Which applications use Hadoop?

Hadoop is an important big data framework that has established itself in many companies and applications in recent years. In general, it can be used primarily for the storage of large and unstructured data volumes and, thanks to its distributed architecture, is particularly suitable for data-intensive applications that would not be manageable with traditional databases.

Typical use cases for Hadoop include:

Big data analysis: Hadoop enables companies to centrally collect and store large amounts of data from different systems. This data can then be processed for further analysis and made available to users in reports. Both structured data, such as financial transactions or sensor data, and unstructured data, such as social media comments or website usage data, can be stored in Hadoop.
Log analysis & IT monitoring: In modern IT infrastructure, a wide variety of systems generate data in the form of logs that provide information about the status or log certain events. This information needs to be stored and reacted to in real-time, for example, to prevent failures if the memory is full or the program is not working as expected. Hadoop can take on the task of data storage by distributing the data across several nodes and processing it in parallel, while also analyzing the information in batches.
Machine learning & AI: Hadoop provides the basis for many machine learning and AI models by managing the data sets for large models. In text or image processing in particular, the model architectures require a lot of training data that takes up large amounts of memory. With the help of Hadoop, this storage can be managed and operated efficiently so that the focus can be on the architecture and training of the AI algorithms.
ETL processes: ETL processes are essential in companies to prepare the data so that it can be processed further or used for analysis. To do this, it must be collected from a wide variety of systems, then transformed and finally stored in a data lake or data warehouse. Hadoop can provide central support here by offering a good connection to different data sources and allowing Data Processing to be parallelized across multiple servers. In addition, cost efficiency can be increased, especially in comparison to classic ETL approaches with data warehouses.

The list of well-known companies that use Hadoop daily and have made it an integral part of their architecture is very long. Facebook, for example, uses Hadoop to process several petabytes of user data every day for advertisements, feed optimization, and machine learning. Twitter, on the other hand, uses Hadoop for real-time trend analysis or to detect spam, which should be flagged accordingly. Finally, Yahoo has one of the world’s largest Hadoop installations with over 40,000 nodes, which was set up to analyze search and advertising data.

What are the advantages and disadvantages of Hadoop?

Hadoop has become a powerful and popular big data framework used by many companies, especially in the 2010s, due to its ability to process large amounts of data in a distributed manner. In general, the following advantages arise when using Hadoop:

Scalability: The cluster can easily be scaled horizontally by adding new nodes that take on additional tasks for a job. This also makes it possible to process data volumes that exceed the capacity of a single computer.
Cost efficiency: This horizontal scalability also makes Hadoop very cost-efficient, as more low-cost computers can be added for better performance instead of equipping a server with expensive hardware and scaling vertically. In addition, Hadoop is open-source software and can therefore be used free of charge.
Flexibility: Hadoop can process both unstructured data and structured data, offering the flexibility to be used for a wide variety of applications. It offers additional flexibility by providing a large library of components that further extend the existing functionalities.
Fault tolerance: By replicating the data across different servers, the system can still function in the event of most hardware failures, as it simply falls back on another replication. This also results in high availability of the entire system.

These disadvantages should also be taken into account.

Complexity: Due to the strong networking of the cluster and the individual servers in it, the administration of the system is rather complex, and a certain amount of training is required to set up and operate a Hadoop cluster correctly. However, this point can be avoided by using a cloud connection and the automatic scaling it contains.
Latency: Hadoop uses batch processing to handle the data and thus establishes latency times, as the data is not processed in real-time, but only when enough data is available for a batch. Hadoop tries to avoid this with the help of mini-batches, but this still means latency.
Data management: Additional components are required for data management, such as data quality control or tracking the data sequence. Hadoop does not include any direct tools for data management.

Hadoop is a powerful tool for processing big data. Above all, scalability, cost efficiency, and flexibility are decisive advantages that have contributed to the widespread use of Hadoop. However, there are also some disadvantages, such as the latency caused by batch processing.

Does Hadoop have a future?

Hadoop has long been the leading technology for distributed big data processing, but new systems have also emerged and become increasingly relevant in recent years. One of the biggest trends is that most companies are turning to fully managed cloud data platforms that can run Hadoop-like workloads without the need for a dedicated cluster. This also makes them more cost-efficient, as only the hardware that is needed has to be paid for.

In addition, Apache Spark in particular has established itself as a faster alternative to MapReduce and is therefore outperforming the classic Hadoop setup. It is also interesting because it offers an almost complete solution for AI workloads thanks to its various functionalities, such as Apache Streaming or the machine learning library.

Although Hadoop remains a relevant big data framework, it is slowly losing importance these days. Even though many established companies continue to rely on the clusters that were set up some time ago, companies that are now starting with big data are using cloud solutions or specialized analysis software directly. Accordingly, the Hadoop platform is also evolving and offers new solutions that adapt to this zeitgeist.

Who should still learn Hadoop?

With the rise of cloud-native data platforms and modern distributed computing frameworks, you might be wondering: Is Hadoop still worth learning? The answer depends on your role, industry, and the scale of data you work with. While Hadoop is no longer the default choice for big data processing, it remains highly relevant in many enterprise environments. Hadoop could be still relevant for you if at least one of the following is true for you:

Your company still has a Hadoop-based data lake.
The data you are storing is confidential and needs to be hosted on-premises.
You work with ETL processes, and data ingestion at scale.
Your goal is to optimize batch-processing jobs in a distributed environment.
You need to work with tools like Hive, HBase, or Apache Spark on Hadoop.
You want to optimize cost-efficient data storage and processing solutions.

Hadoop is definitely not necessary for every data professional. If you’re working primarily with cloud-native analytics tools, serverless architectures, or lightweight data-wrangling tasks, spending time on Hadoop may not be the best investment.

You can skip Hadoop if:

Your work is focused on SQL-based analytics with cloud-native solutions (e.g., BigQuery, Snowflake, Redshift).
You primarily handle small to mid-sized datasets in Python or Pandas.
Your company has already migrated away from Hadoop to fully cloud-based architectures.

Hadoop is no longer the cutting edge technology that it once was, but it still has importance in different applications and companies with existing data lakes, large-scale ETL processes, or on-premises infrastructure. In the following part, we will finally be more practical and show how an easy cluster can be set up to build your big data framework with Hadoop.

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The software can block domains associated with phishing, malware, botnets, and other high-risk categories such as cryptomining or new domains that haven’t been reported previously. It can also create custom block and allow lists and offers the ability to pinpoint compromised systems using real-time security activity reports, Brunetto wrote. According to Cisco, many organizations leave DNS resolution to their ISP. “But the growth of direct enterprise internet connections and remote work make DNS optimization for threat defense, privacy, compliance, and performance ever more important,” Cisco stated. “Along with core security hygiene, like a patching program, strong DNS-layer security is the leading cost-effective way to improve security posture. It blocks threats before they even reach your firewall, dramatically reducing the alert pressure your security team manages.” “Unlike other Secure Service Edge (SSE) solutions that have added basic DNS security in a ‘checkbox’ attempt to meet market demand, Cisco Secure Access – DNS Defense embeds strong security into its global network of 50+ DNS data centers,” Brunetto wrote. “Among all SSE solutions, only Cisco’s features a recursive DNS architecture that ensures low-latency, fast DNS resolution, and seamless failover.”

HPE Aruba unveils raft of new switches for data center, campus modernization

And in large-scale enterprise environments embracing collapsed-core designs, the switch acts as a high-performance aggregation layer. It consolidates services, simplifies network architecture, and enforces security policies natively, reducing complexity and operational cost, Gray said. In addition, the switch offers the agility and security required at colocation facilities and edge sites. Its integrated Layer 4 stateful security and automation-ready platform enable rapid deployment while maintaining robust control and visibility over distributed infrastructure, Gray said. The CX 10040 significantly expands the capacity it can provide and the roles it can serve for enterprise customers, according to one industry analyst. “From the enterprise side, this expands on the feature set and capabilities of the original 10000, giving customers the ability to run additional services directly in the network,” said Alan Weckel, co-founder and analyst with The 650 Group. “It helps drive a lower TCO and provide a more secure network.” Aimed as a VMware alternative Gray noted that HPE Aruba is combining its recently announced Morpheus VM Essentials plug-in package, which offers a hypervisor-based package aimed at hybrid cloud virtualization environments, with the CX 10040 to deliver a meaningful alternative to Broadcom’s VMware package. “If customers want to get out of the business of having to buy VM cloud or Cloud Foundation stuff and all of that, they can replace the distributed firewall, microsegmentation and lots of the capabilities found in the old VMware NSX [networking software] and the CX 10k, and Morpheus can easily replace that functionality [such as VM orchestration, automation and policy management],” Gray said. The 650 Group’s Weckel weighed in on the idea of the CX 10040 as a VMware alternative:

Indian startup Refroid launches India’s first data center CDUs

They use heat exchangers and pumps to regulate the flow and temperature of fluid delivered to equipment for cooling, while isolating the technology cooling system loop from facility systems. The technology addresses limitations of traditional air cooling, which industry experts say cannot adequately handle the heat generated by modern AI processors and high-density computing applications. Strategic significance for India Industry analysts view the development as a critical milestone for India’s data center ecosystem. “India generates 20% of global data, yet contributes only 3% to global data center capacity. This imbalance is not merely spatial — it’s systemic,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “The emergence of indigenously developed CDUs signals a strategic pivot. Domestic CDU innovation is a defining moment in India’s transition from data centre host to technology co-creator.” Neil Shah, VP for research and partner at Counterpoint Research, noted that major international players like Schneider, Vertiv, Asetek, Liquidstack, and Zutacore have been driving most CDU deployments in Indian enterprises and data centers. “Having a local indigenous CDU tech and supplier designed with Indian weather, infrastructure and costs in mind expands options for domestic data center demand,” he said. AI driving data center cooling revolution India’s data center capacity reached approximately 1,255 MW between January and September 2024 and was projected to expand to around 1,600 MW by the end of 2024, according to CBRE India’s 2024 Data Center Market Update. Multiple market research firms have projected the India data center market to grow from about $5.7 billion in 2024 to $12 billion by 2030. Bhavaraju cited aggressive projections for the sector’s expansion, with AI workloads expected to account for 30% of total workloads by 2030. “All of them need liquid cooling,” he said, noting that “today’s latest GPU servers – GB200 from Nvidia

Platform approach gains steam among network teams

Revisting the platform vs. point solutions debate The dilemma of whether to deploy an assortment of best-of-breed products from multiple vendors or go with a unified platform of “good enough” tools from a single vendor has vexed IT execs forever. Today, the pendulum is swinging toward the platform approach for three key reasons. First, complexity, driven by the increasingly distributed nature of enterprise networks, has emerged as a top challenge facing IT execs. Second, the lines between networking and security are blurring, particularly as organizations deploy zero trust network access (ZTNA). And third, to reap the benefits of AIOps, generative AI and agentic AI, organizations need a unified data store. “The era of enterprise connectivity platforms is upon us,” says IDC analyst Brandon Butler. “Organizations are increasingly adopting platform-based approaches to their enterprise connectivity infrastructure to overcome complexity and unlock new business value. When enhanced by AI, enterprise platforms can increase productivity, enrich end-user experiences, enhance security, and ultimately drive new opportunities for innovation.” In IDC’s Worldwide AI in Networking Special Report, 78% of survey respondents agreed or strongly agreed with the statement: “I am moving to an AI-powered platform approach for networking.” Gartner predicts that 70% of enterprises will select a broad platform for new multi-cloud networking software deployments by 2027, an increase from 10% in early 2024. The breakdown of silos between network and security operations will be driven by organizations implementing zero-trust principles as well as the adoption of AI and AIOps. “In the future, enterprise networks will be increasingly automated, AI-assisted and more tightly integrated with security across LAN, data center and WAN domains,” according to Gartner’s 2025 Strategic Roadmap for Enterprise Networking. While all of the major networking vendors have announced cloud-based platforms, it’s still relatively early days. For example, Cisco announced a general framework for Cisco

Oracle to spend $40B on Nvidia chips for OpenAI data center in Texas

OpenAI has also expanded Stargate internationally, with plans for a UAE data center announced during Trump’s recent Gulf tour. The Abu Dhabi facility is planned as a 10-square-mile campus with 5 gigawatts of power. Gogia said OpenAI’s selection of Oracle “is not just about raw compute, but about access to geographically distributed, enterprise-grade infrastructure that complements its ambition to serve diverse regulatory environments and availability zones.” Power demands create infrastructure dilemma The facility’s power requirements raise serious questions about AI’s sustainability. Gogia noted that the 1.2-gigawatt demand — “on par with a nuclear facility” — highlights “the energy unsustainability of today’s hyperscale AI ambitions.” Shah warned that the power envelope keeps expanding. “As AI scales up and so does the necessary compute infrastructure needs exponentially, the power envelope is also consistently rising,” he said. “The key question is how much is enough? Today it’s 1.2GW, tomorrow it would need even more.” This escalating demand could burden Texas’s infrastructure, potentially requiring billions in new power grid investments that “will eventually put burden on the tax-paying residents,” Shah noted. Alternatively, projects like Stargate may need to “build their own separate scalable power plant.” What this means for enterprises The scale of these facilities explains why many organizations are shifting toward leased AI computing rather than building their own capabilities. The capital requirements and operational complexity are beyond what most enterprises can handle independently.

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.

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

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

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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