Is Hadoop Dead? The Future of Big Data Analytics and its Replacements

   

As a complete open-source big data suite, Apache Hadoop has profoundly influenced the entire big data world over the past decade. However, with the development of various emerging technologies, the Hadoop ecosystem has undergone tremendous changes. Is Hadoop still relevant? or is big data dead? If so, what products/technologies will replace it? What is the future outlook for big data analysis?

 

This article will analyze: 

 

1. 1. The history of Hadoop & its open-source ecosystem
2. 2. The emerging technology options under the cloud-native trends
3. 3. The future outlook of big data analysis in the next 10 years

   

Understanding Hadoop: A Brief History of Big Data and Its Role

 

For the past two decades, we have been living in an era of data explosion. The amount of data created in traditional business, such as orders and warehousing, has increased relatively slowly, and its proportion in the total amount of data has gradually decreased.

   

In recent times, there has been a remarkable surge in the collection and storage of vast amounts of data. This includes not only human-generated information but also data from machines, such as logs and IoT devices, which far exceeds the volume of traditional business data.

This rapid accumulation of data has created a significant technological challenge. The sheer volume of information has outpaced the human ability to process it, leading to the development of various big data technologies. This scenario marks the advent of what we know as the era of big data.

What is Hadoop? 

Hadoop is an open-source software framework that stores and processes large amounts of data. It is based on the MapReduce programming model, which allows for the parallel processing of large datasets.

Hadoop is used for big data and analytics jobs. It breaks workloads down into smaller workloads that can be run at the same time. Hadoop allows clustering multiple computers to analyze massive datasets in parallel more quickly.

 

2006: The Rise of Apache Hadoop for Big Data Processing

 

Thanks to “big data” and the influential Apache community of open-source software projects, Hadoop has become rapidly popular, and many commercial companies have emerged.

 

Hadoop is such a fully functional big data processing platform. It contains a variety of components to meet different functional requirements, such as:

 

• HDFS for data storage
• Yarn for resource management
• MapReduce and Spark for data calculation and processing
• Sqoop for relational data collection
• Kafka for real-time data pipelines
• HBase for online data storage and access
• Impala for online ad-hoc queries, etc.

   

Hadoop used clusters for parallel computing soon after its birth, breaking the sorting record held by supercomputers. It has been widely adopted by companies and various organizations with proven strength.

 

Top Hadoop big data solutions in the market include three vendors — Cloudera, Hortonworks, and MapR. In addition, public cloud vendors also provide hosted Hadoop services on the cloud, such as AWS EMR, Azure HDinsight, etc., which account for the majority of Hadoop’s market share.

 

2018: Market Shifts with the Cloudera and Hortonworks Merger

 

In 2018, the market saw significant shifts. The Hadoop ecosystem was notably impacted by a major development: the merger of Cloudera and Hortonworks.

 

 

Essentially, the leading two players in the market joined forces to maintain their positions. Following this, HPE announced its acquisition of MapR. These mergers and acquisitions signified a challenging reality: despite Hadoop's widespread popularity, the involved companies struggled with operational challenges and found profitability elusive.

 

Following its merger with Hortonworks, Cloudera announced a significant change in its policy: all of its product lines, including those that were previously open-source, will now require payment. This means open-source products, once freely accessible to everyone, are now exclusively available to paying customers.

   

The HDP distribution, which has been available for free in the past, is no longer maintained and available for download. It will be merged into a unified CDP platform in the future.

 

2021: Observing the Decline of Hadoop's Open-Source Ecosystem

 

In April 2021, the Apache Software Foundation announced the retirements of 13 big data-related projects, 10 of which are part of the Hadoop ecosystem, such as Eagle, Sentry, Tajo, etc. 

 

Now, Apache Ambari, born with the mission to manage Hadoop clusters, becomes the first Apache project to be retired in 2022.

 

2022 and Beyond: Envisioning the Post-Hadoop Era in Big Data

 

Will Hadoop ultimately be abandoned? I believe this will not happen anytime soon. Hadoop is still relevant. After all, the widespread use of Hadoop by a large user base implies significant costs associated with migrating platforms and applications.

 

Therefore, the current users will continue to use it, but the number of new users will gradually decrease. This is what we call the “post-Hadoop era”.

   

Regarding Apache Hadoop's future growth, the outlined development path originates from a Hadoop community meetup. Post version 3.0, it's evident that the newer additions to Hadoop haven't been particularly groundbreaking. The focus has shifted primarily to integrating with Kubernetes (K8s) and Docker, which may not be highly appealing to those deeply involved in big data fields.

 

What Led to Hadoop's Decline?

 

Google Trends shows that interest in Hadoop reached its peak popularity from 2014 to 2017. After that, we see a clear decline in searches for Hadoop. It’s not unexpected that Hadoop has been gradually losing its aura. Any technology, by the way, will go through the cycle of development, maturity, and decline, and no technology can escape the objective law.

 

When looking at the current state of Apache Hadoop and it’s ecosystem there are a few key factors pointing to it’s eventual demise.

 

New Market Demands for Data Analytics and Emerging Technologies

Looking back at the development history of Hadoop, it can be seen that the software framework has emerged because of the strong demand for big data storage. In the present day, however, users have new demands for data management and analysis, such as online rapid analysis, separation of storage and computing, or AI/ML for artificial intelligence and machine learning.

 

In those respects, Hadoop can only provide limited support. In this regard, it cannot be compared with some emerging technologies. For example, Redis, Elastisearch, and ClickHouse, which have been very popular in recent years, can all be applied to big data analysis.

 

For customers, there is just no need to deploy the complex Hadoop platform if a single technology can meet their demand.

 

The Impact of Fast-growing Cloud Vendors and Services on Hadoop's Relevance

From another perspective, cloud computing has been developing rapidly in the past decade or so, not only beating traditional software vendors such as IBM, HP, etc. but also encroaching to a certain extent on the big data market of Hadoop.

 

In the early days, cloud vendors only deployed Hadoop on IaaS, such as AWS EMR (claimed to be the most deployed Hadoop cluster in the world). For users, the Hadoop services hosted on the cloud can be started and stopped at any time, and the data can be safely backed up on the cloud vendor’s data service platform, which is easy to use and cost-saving.

   

Besides that, cloud vendors render a range of big data services for specific scenarios to form a complete ecosystem, such as persistent and low-cost data storage implemented by AWS S3, KV data storage, and access with low latency implemented by Amazon DynamoDB, Athena, a serverless query service to analyze big data, etc.

 

Examining the Increasing Complexity of the Hadoop Ecosystem

In addition to the emerging technologies and cloud vendors that continue to offer new services, Hadoop itself has been gradually showing “fatigue”. Building blocks is a good option. However, it increases the difficulty for users to use the components of the Hadoop ecosystem.

   

As can be seen from the figure above, there have been 13 (if not more) commonly used components in Hadoop, posing a huge challenge to Hadoop users in terms of learning and O&M.

 

The Impact of Cloudera and Hortonworks' Strategy on Hadoop's Popularity

Technology vendor Cloudera/Hortonworks can’t release a high-quality free product on the market. It turns out that their earlier two-pronged “free version + paid version” approach doesn’t work. Cloudera will only offer the paid version of CDP in the future, indicating the end of the free lunch. It is unknown whether other manufacturers are willing to offer free products. Even if there is such a manufacturer, its product stability and sophistication are yet unknown. After all, the core developers of Hadoop mostly work for Cloudera and Hortonworks.

 

Analyzing the Inconsistent Quality of Hadoop’s Open-Source Ecosystem

 

Don’t forget that Hadoop is an open-source project hosted by the Apache Foundation. Apache is designed for the public good, which can be obtained, used, and distributed by the public for free. So if you don’t want to pay for it, there is an option called Apache Hadoop available for free use. After all, a large number of Internet companies still use Apache Hadoop (at their scale, only the open-source version can be used). If they can, why can’t I?

 

However, open-source software is of average quality, with no service and no SLA guarantees. Users can only find out and solve problems on their own. They have to post questions in the community and wait for the results. If you’re okay with that, then hire a few engineers to try it out. By the way, Hadoop development or O&M engineers are hard and expensive to hire.

 

Looking Beyond Hadoop: Exploring Alternative Solutions for Big Data

 

Kyligence emerges as a formidable alternative to Hadoop in the cloud analytics arena, offering a new path for users navigating the shift from the traditional Hadoop ecosystem.

This transition requires careful consideration of both budget and technical capabilities.

 

Key Success Factors for Hadoop Alternatives

 

How do solutions in the Hadoop ecosystem respond to the new era? The evolution history of Apache Kylin and Kyligence are perfect examples.

 

Both the Apache Kylin project and Kyligence were born in the Hadoop era. Initially, all Kyligence products ran on Hadoop. About 4 years ago, Kyligence foresaw that customer needs were slowly shifting to cloud-native and the separation of storage and computing.

 

Having seen such industry trends, Kyligence made a large transformation to its original platform system.

 

 

Key Features of Kyligence Post-Hadoop Transition:

• Utilizes a cloud-native architecture at its core.
• Incorporates object storage services from various cloud vendors, including AWS S3 and ADLS, for data storage.
• Employs Apache Spark+ and containerization for computational processes.
• Allows direct integration with IaaS services and ECS on cloud platforms.
• Expanded reach to multiple cloud environments, enhancing versatility.
• Continuously refined architecture for optimal performance.

 

The recent update to Kyligence's architecture has brought substantial improvements in terms of flexibility and ease of maintenance. This has led to a considerable decrease in the Total Cost of Ownership (TCO), earning high praise in the market.

 

At Kyligence, our team understands that our success depends on our ability to swiftly and insightfully identify new trends and innovate our offerings.

 

In the fast-paced and highly competitive big data and analytics sector, it's crucial for us at Kyligence to keep a close watch on market developments, pay attention to our users' feedback, and tailor our products to meet these needs. We are dedicated to the ongoing development of Kyligence, making sure it stays at the forefront of technological advancements.

 

Future Trends in Big Data and Analytics

 

In conclusion, "Is Hadoop dead?" and "Is Big Data dead?" encapsulate the industry's evolving dynamics. While Hadoop may not hold the same prominence, its foundational concepts continue to resonate. Similarly, Big Data's essence remains indispensable, albeit through new lenses and technologies. The transition towards cloud-native solutions indicates not an end but a transformation, ensuring the continual relevance and growth of Big Data analysis in the face of changing market demands. Through these lenses, the death of Hadoop and Big Data seems more of a metamorphosis, paving the path for the next wave of innovation in data analytics.

 

Technology will keep progressing, startups with new missions may come and go, and major corporations remain resilient. I’ve written a blog about 7 must-know data buzzwords, and discussed emerging trends in 2022. To keep this one short, I’ll just list some (not all) examined interesting trends, and share with you some nice articles around them:

• Metrics Store will rise as the ultimate solution to keep the “single source of truth” - The missing piece of the modern data stack
• The design concept of Data Mesh will continue to influence IT decision-makers - Whitepaper: The Data Mesh Shift
• Data API/Data-as-a-product is an abstract but real demand from the market - Every product will be a data product; Big Data, Cloud, AI, and Data Analytics Predictions for 2022

 

FAQ on Apache Hadoop in Big Data Analytics

 

We cover critical questions our customers often ask on Hadoop in analyzing big data

 

What is Hadoop Used For in Big Data Analytics?

Apache Hadoop is an open-source software framework used for distributed storage and processing of large datasets. Hadoop's primary role in Big Data Analytics involves efficiently handling vast amounts of data, offering scalability, fault-tolerance, and cost-effective solutions. Hadoop can also facilitate advanced analytics, such as predictive analytics, data mining, and machine learning.

 

What are the Core Components and Use-Cases of Apache Hadoop?

The core components of Apache Hadoop include the Hadoop Distributed File System (HDFS) for data storage, MapReduce for processing, and YARN for resource management. Its main use cases involve processing and storing massive data sets, conducting large-scale data analysis, and supporting advanced data-driven applications in industries such as healthcare, finance, retail, and telecommunications.

What does Cloudera do?

Cloudera provides a hybrid data platform called Cloudera Data Platform, catering to modern data architectures and enabling data management and analytics anywhere. It encompasses various services, including DataFlow, Stream Processing, Data Engineering, Data Warehouse, Operational Database, Machine Learning, and Data Hub.

Cloudera has a deep-rooted relationship with Hadoop, as it was one of the first companies to commercialize Apache Hadoop. Cloudera provides a distribution of Hadoop, which is often referred to as "Cloudera Hadoop". This distribution enhances the usability and management of Hadoop ecosystems, integrating various components to facilitate data storage, processing, and analytics. Through Cloudera Hadoop, users can harness the power of big data technologies while benefiting from Cloudera's support and management tools.