As digitalization advances and data continues to grow, business intelligence and analytics leaders are challenged to create state-of-the-art solutions that accommodate fast-evolving business models. New technologies for distributed computing like Spark are addressing these requirements for managing, processing, analyzing massive amounts of data.
In its newest release, biGENiUS now simplifies and accelerates the development, integration and maintenance of big data solutions based on Spark. Through advanced automation and system support, organizations can now unlock the value of big data in an agile and cost-efficient way.
Today, we are experiencing an explosion of data — in volume, velocity and variety. This accelerated growth of both structured and unstructured data has motivated many business intelligence and analytics stakeholders to modernize their inflexible data warehouses and consider newer technologies to accommodate these workloads.
According to the TDWI Best Practices Report, the main driver for data warehouse (DWH) modernization is increasing the scale for Big Data. In creating scalable, hybrid and clustered environments, more and more companies move toward concepts like Data Lake and platforms like Hadoop, NoSQL, etc. Additionally, by using the resources of big data clusters, organizations reduce costs for storage, computing and licensing.
Another technology, which has taken on a central role within the big data strategies of many organizations, is Spark. Originally developed at UC Berkeley's AMPLab and later offered to the Apache Software Foundation, Spark is designed to process vast volumes of data with speed, simplicity and flexibility. Over the last years, the open source framework has undergone extensive development and is today often being referred to as the Swiss Army Knife of Big Data Analytics.
But what is Spark?
Apache Spark is a fast and general engine for big data processing with built-in options for streaming, SQL, machine learning, graph processing and more. It gives a comprehensive, unified framework to manage big data requirements for a variety of datasets (based on databases, files, graph data, etc.) as well as the source of data (batch v. real-time streaming data).
Considered to be one of its major advantages is the fact, that Spark utilizes in-memory capabilities to process massive amounts of data in a short span of time on large clusters. Thus, it offers impressive performance up to 100 faster than well-known MapReduce.
Spark’s distinguishing feature are its Resilient Distributed Datasets (RDDs). An RDD is an immutable collection of objects, that automatically rebuilds on failure. In an RDD, data is partitioned and each partition is fed to a different node across a cluster, which allows operations on the RDD to be done in parallel.
Spark can be run on top of major storages / resource managers like Hadoop/Yarn and Mesos, in the cloud and even standalone. Therefore, it can be easily operated on a commercial PC or notebook for testing and development purposes. Its Data Sources API allows Spark to access data on wide range of input sources including HDFS, S3, SQL and NoSQL databases like Cassandra and HBase.
Automation for Big Data Platforms
In its latest release, biGENiUS offers a built-in Spark generator, which boots the development and maintenance of big data platforms. You can now automate the creation and management of separate DWH objects, entire DWH layers or the complete data warehouse including all the necessary ETL processes for your big data solution using one single tool.
By providing automation for Spark applications, with biGENiUS you can now:
- process vast amounts of data simultaneously on hundreds or thousands of computing nodes
- unlock new possibilities for data volume, computing performance and flexibility
- integrate and process various big data formats
- reduce costs for storage, computation and licencing by using HDFS or/and NoSQL
- generate clean valuable metadata along with up-to-date documentation without any additional effort
biGENiUS helps you exceed the limitations of the traditional data warehouse. You can build your own data lake and easily integrate your existing analytical solution. Automation lets you improve agility, sandbox new approaches and iterate faster than ever before. As a result, you reduce the time-to-market for new big data solutions to its minimum and unlock new potentials of your organisation's data.