Big Data on Steroids with Apache Spark
As big data takes center stage in the new data explosion, Hadoop has emerged as one the leading technologies addressing the challenges in the space. As the data processing needs of enterprises are growing newer technologies like Apache Spark have emerged as significant options that consistently offer expanded capabilities for the big data space. As these enterprise needs are met, so is the increased appetite for faster processing, low latency requirements for high velocity data and an iterative demand for processing where leading technologies like Hadoop fall short of expectations or at times seem cumbersome to implement due to its inherent design.
Delivering on this growing need of enterprises is where Spark plays a very crucial role and is emerging as the platform of choice. Why? Apache Spark offers built in support for in-memory processing, support for HDFS and the ability to work with data using SQL unlike any other open source big data technology and much more, all along offering superior performance gains over traditional choices. Spark thus supercharges the big data processing landscape and is truly a powerful complementary technology stack that reinforces the big data platform for enterprises, offering an expanded ability to do far more than Hadoop alone could deliver upon, thus the term Big Data on steroids with Spark.
Apervi Conflux Director™ & Databricks
Apervi, a leading innovator in the space of data engineering for big data, is completely focused on making it easy for users to leverage the benefits of big data technologies without the complexity associated with the respective technologies (namely Hadoop, Storm, & Spark). In the same spirit Apervi identified Spark very early on as one of the strong offerings capable of catering to the growing needs of enterprises to process diverse forms of data beyond the limitations of MapReduce for superior performance. Apervi started work to build support for Spark within its offering the Conflux Director™, a unified orchestration platform for big data, to quickly expand what users can do with their data without being bound by the processing paradigm limitations, be it batch / real-time. By offering support for Spark standard within Conflux Director™, Apervi is able to deliver the power of Spark readily to its users to build high performance data engineering workflows in an intuitive fashion consistent with its support for other Big Data technologies, all without a steep learning curve.
Spark and Conflux for Telecom
Spark support in Conflux has enabled one of our customers in the telecom industry to quickly build and test the viability of a real-time promotion targeting program of wireless customers based on location and status in a relatively short period of time. Combining Spark with Conflux for ETL and stream processing has enabled this customer to compress processing windows drastically, realizing swift gains, and conduct rapid prototyping to A/B test promotion effectiveness efficiently.
Conflux “Certified on Spark”
As we progressed in our journey of building support for Spark, the introduction of the Databricks “Certified on Spark “ program got us really excited. It allowed us to both showcase our close integration with Spark and highlight the need to promote Spark to the user community in a systematic fashion. We are proud and excited to be participating in the “Certified on Spark “ program by Databricks because we feel that working with Databricks will help the Spark ecosystem grow, thus encouraging broader adoption of such a fantastic technology to meet the diverse needs of our customers to tame the challenges of big data.
So, it is with both pride — as well as thanks to the team at Databricks — that we announce that Conflux Director™ is now “Certified on Spark”.
Apervi will continue to build tools that help make Spark easy to use and support Databricks in its mission. We’ll strive to make the developer experience seamless between our tools and the Spark technology stack thus delivering on the broader promise of enabling enterprises to derive value from Big Data easier and faster.