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The Perfect Combination: Hadoop and SAP HANA

The Perfect Combination: Hadoop and SAP HANA


The combination of SAP HANA and Apache Hadoop provides a potent solution for real-time analytics and managing large datasets. This collaboration allows organizations to gain insights, process data swiftly, analyze streaming data, and access real-time data across various sources. SAP Analytics solutions integrate with Hadoop components like Impala, Yarn, Spark, and Hive for tasks such as data wrangling and predictive analysis. While SAP HANA excels with structured in-memory data, Hadoop's open-source nature and distributed architecture offer advantages. Together, they enhance analytics, unlocking real-time insights from vast datasets.

SAP-HANA (High-Performance Analytic Appliance) is making its presence felt as a scalable and robust memory column-oriented database management system for providing real-time analytics. Likewise, Hadoop, an open-source technology platform, adequately supports the processing and analysis of large sets of unstructured, semi-structured and structured data for managing massive volumes of varied datasets. The combination of SAP HANA with Hadoop is helping businesses attain and harness the plus points of both, and how. 

Read on for why your organization should also look towards the many advantages of Apache Hadoop and SA HANA. 

Apache Hadoop with SAP HANA

Big Data, when appropriately geared with the SAP HANA platform and its analytics database, events stream processing applications, data services, and Apache Hadoop, goes a long way in aiding organizations like yours, and in many more ways than one (Here's the perfect parcel of information to learn data science). With this combine, you can:

  • Convert large volumes of data into meaningful insights more effectively.

  • Gain accurate, relevant and fast insights, along with running processes that are over 10,000 to 100,000 times quicker in memory.

  • Analyze streaming data and store significant events in real-time for the purposes of deeper analysis. 

  • Virtualize access to real-time data across various data stores for gaining further insight without shifting the data.

  • Mine large volumes of data and get access to insights for finding relevant information.

  • Extract, effectively transform and load your enterprise data across numerous stores to attain a comprehensive view of the same. 

Ways in which Hadoop works with SAP

SAP Analytics solutions in themselves, or via projects such as Impala, Yarn, Spark, and Hive, are capable of allowing data wrangling, accessing HDFS stores, visualizing/reporting, and gaining predictive analysis on the data held in Hadoop. For instance, SAP Lumira--SAP’s data discovery application--works well with Hortonworks Hadoop Sandbox. All in all, SAP Data Services are equipped for interacting with Hadoop and SAP HANA platforms with the help of projects such as Pig and Hive; they are immensely helpful in moving, transforming and gaining insights from data.
From virtualization and federation to streaming data, the SAP HANA platform is being used for leveraging the Hadoop ecosystem in many ways. These days, Big Data based organizations are pushing queries into Hadoop, getting resultant sets, and kicking off MapReduce with the seamless integration of HANA’s in-memory speed engines and libraries, and so forth. SAP HANA is also applying the distributed processing tools and mass storage of Hadoop for greater benefits. For instance:

  • SAP supports and resells various Hadoop distributions such as Hortonworks.

  • SAP products and Hadoop help in driving a tighter integration by including the tools of HANA Cloud Platform (HCP) and so forth. 

  • It is now possible to investigate synergies in line with an organization’s information lifecycle management processes and support enterprise compliance projects for the overall adoption of Hadoop. 

Hadoop along with SAP Technology

There are some major differences that exist between these technologies. While Hadoop is known to use commodity servers for handling data sizes beyond the 100 TB range (or less), traditional relational database management systems (RDBMS) and SAP HANA handles other data sizes very well. But then, as the current versions of Hadoop tend to be significantly slower than conventional RDBMs, and SAP HANA, they take a long time in providing analytic results. As these versions are designed to handle arbitrary data structures easily, they end up with hardware storage costs per terabyte

SAP HANA/ In-Memory vs. Hadoop

The act of choosing the appropriate data technology for OLTP or analytical solutions requires an in-depth understanding of the differences between Hadoop and SAP HANA. The table below explains some of the fundamental distinctions between the two: 

SAP HANA Database Hadoop

Mainly structured data in memory

Any file or data structure on disk

License fees required

No fees—open source

Shortage of IT skills

Shortage of IT skills

Rapid innovation

Rapid innovation

Enterprise—ready administration tools

Few enterprise—ready administration tools

High data consistency based on ACID principles

Eventual data consistency (BASE)

 Excellent OLAP    Slow OLAP     


Excellent OLTP


Database appliances

Commodity servers

Sever level failover

Query and sever level failover

Scale-up/scale-out architecture

Scale-out architecture

1 or many servers (100s of cores)

Distributed servers

Pre-defined schema

No schema/post-defined schema

Very fast access

Very slow data access

Though Hadoop and SAP HANA are best suited for real-time analytics and data updates, the cost outlays have to be figured out in relation to the volumes of data, the ease of access required, and other database technologies in-store. While Hadoop--an open-source software--is sans licensing fees and runs on low-cost commodity servers, the overall expenditure of running a well-designed Hadoop cluster can be very significant (also consider checking out this career guide for data science jobs). This proves to be especially true when thousands of servers have to be managed to gain optimum performance levels. When combined with SAP HANA (which proves to be a better technology for handling specific situations), applications requiring real-time analysis (rather than in-memory computing technology) are helped greatly.


SAP HANA and Hadoop are proving to be good friends. On the one hand, HANA stores high-value, often in the form of user data, while on the other, Hadoop helps in persisting information for retrieval and archival in new ways. HANA can be connected with Hadoop for running batch jobs, loading more information, and performing super-fast aggregations. Overall, these two technology trends are impacting the information infrastructure and helping businesses unlock information via real-time analytics and fast access to large-sized data sets. 


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