A current issue in Big Data is the usage of the Map-Reduce framework, which is intended mainly for batch processing and not ideal for real-time interactive SQL use cases, such as ad-hoc query analysis, machine learning, and other similar applications.
Big Data companies are attempting to solve this issue via several projects such as Cloudera Impala, Hortonworks Stinger, and Pivotal HAWQ, all of which seek to enhance Hive performance somehow.
Apache Spark is another open-source project getting a lot of traction to address the same problems as MapReduce. It accomplishes so by allowing in-memory access to data stored in HDFS, similar to how MapReduce works. In addition, it offers high-level APIs in the programming languages Scala, Java, and Python.
Spark is a general-purpose computing system that is both fast and flexible, and it supports a diverse collection of tools such as Shark (Hive on Spark), Spark SQL, MLlib for machine learning, Spark Streaming, and GraphX for graph processing.
SAP HANA is extending its Big Data solution by integrating it with Apache Spark, which is made possible via the HANA smart data access technology. In this case, an in-memory data fabric architecture provides real-time interactive analysis and applications spanning corporate application data and content stored in HDFS throughout the whole business.
Using in-memory methods, developers and data scientists may quickly and easily create the tools that will allow them to get instant Big Data insights on their customers, suppliers, partners, and products. Furthermore, when SAP HANA and Spark are used together, Apache spark integration of mission-critical applications and analytics with contextual data from Hadoop is significantly simplified, according to the company.
Apache Spark is the most recent data processing framework to emerge from the open-source community. A large-scale data processing engine, it will almost certainly replace Hadoop’s MapReduce soon.
Apache Spark integration and Scala have inextricably linked because the Scala shell is the quickest and most straightforward method to get started with Spark. However, it also supports Java and Python programming languages. A large group of four hundred developers from more than fifty organizations have come together to create applications using Spark. It is undeniably a significant financial commitment.
Integrating The Apache Spark And SAP HANA Creating A Bridge Between The Two Worlds
In conjunction with the proliferation of online apps, social media, and the internet of things, and the widespread digital digitization of corporate operations, the production of raw data has increased exponentially. As a result, enterprises across a wide range of sectors are beginning to see all types of data as strategic assets.
They are increasingly utilizing them to make sophisticated data-driven business choices. Several applications and use-cases are being powered by Big Data solutions, which are being used to create corporate “data lakes,” which store processed or raw data from all accessible sources and power a range of applications and use-cases.
Soon, big data solutions will also be a key component of corporate solutions for predictive analytics and Internet of Things (IoT) deployments. As a result, more interaction between SAP on-premises and SaaS solutions, particularly the HANA platform, will be required in the Apache ecosystem going forward.
The Following Are The Primary Characteristics of SAP HANA:
- A development interface that is available to everyone.
- an in-memory query engine that is built on top of the Apache Spark framework
- Support for the most widely used Spark distributions
- Compile queries for distribution across Apache Distributed File System nodes for faster processing.
- Improved Spark SQL semantics to support hierarchies, allowing for OLAP and drill-down analysis to be performed.
- Improved mash-up application programming interface for faster access to business application data for machine learning workloads.
- It allows for bidirectional communication between Hana and Apache.
Why Does SAP HANA Bring Anything New To The Table?
A deeper examination of both data-processing environments, on the other hand, reveals that they each contribute something unique to the relationship. SAP HANA comes with some extra built-in capabilities that help to improve productivity and security. For example, it provides data management capabilities for storage tiering, which transfers information to the most appropriate kind of storage depending on how often it is accessed. In addition to these capabilities, other SAP HANA data management solutions minimize redundancy, reducing the need for additional storage space.
SAP HANA’s security features make it easier to manage and monitor operations that may take up a significant amount of time and attention from your team. Encryption, identity and access management, a security dashboard, and disaster recovery solutions are all included in the standard business continuity and security package.
The preconfigured and pre-tested nature of this solution distinguishes it from other Power Systems. In addition, it is specifically designed to operate with Apache Spark’s cluster computing. When you combine SAP HANA with Apache Spark integration, you can take your data processing to a whole new level. By putting both solutions into POWER mode, you will be propelled into the stratosphere of achievement.