These Emerging Data Analytics Tools are Beyond Apache Spark All is changing a lot in the Data Analytics landscape, and it is thrilling to see open-source tools leading it. You are surely by now, familiar with the buzzwords in this space, Hadoop, Apache Spark, and many more, but there are some unsung heroes just getting around the corner to take on the world of Data Analytics. There is a strongly growing need for new tools in the space to holistically complete the curve of Data analytics. What is interesting is that a lot of these tools are customized to process the Streaming data. Internet of Things (IoT), the technology that is getting sensors installed in all things, and getting a regular input of data from these sources, is the one factors that have led to the need for new analytics tools. Streaming Data Analytics is something that is imperative for drug discovery, and the SETI Institute and NASA are collaborating to churn and read terabytes of complex and streaming deep space radio signal data. While Apache Spark is the talk of the town, these are the Technologies in the same space that will soon be recognized for their strengths
Grappa – Big and small organizations are in the continuous strife to make something useful out of otherwise gibberish data, much of which comes from clusters and commodity hardware. This requires affordable datacentric approaches that can improve the performance and functionality of technology tools like Apache Spark and MapReduce. The open-source project Grappa is the answer. It scales data-intensive applications on commodity clusters and provides a newer class of abstraction that can combat the classic distributed shared memory (DSM) systems. The developers of Grappa note that it provides a high-level abstraction such that it already includes a number of performance optimizations inherently. They also reveal that prototypical implementations of simplified MapReduce, GraphLab, and a relational query engine have been built on the platform, all of which outperform the original systems.
Apache Drill – The Apache Drill project is making so much of disruption and advancements in the Data Analytics community that companies like MapR have wrapped it into their Hadoop Distributions. Apache Drill is a top-level project at Apache and is being utilized with a lot of data streaming scenarios with Apache Spark. Drill is being hailed in the Streaming space as it is a schema-free SQL engine and a distributed framework. Drill can be used by the DevOps and IT staff members to interactively explore and dig out data in Hadoop and other NoSQL databases like HBase and MongoDB. Drill has the potential to automatically leverage the structure that is inherent in the data and so there is no need of defining and maintaining an explicit schema. It streams data in memory between the operators and reduces the use of disks unless very essential to complete a query.
Apache Kafka – Apache Kafka is being dubbed as a star for real-time data tracking capabilities. It also provides high-throughput, unified, and low-latency processing for data in real-time. The organization, Confluent, has
produced custom tools for using Kafka with data streams. Originally developed by LinkedIn and open-sourced in 2011, Apache Kafka is a comprehensively tested and hardened tool. Many organizations today are in need of people with knowledge about the growing platform. Cisco, Netflix, Uber, PayPal, and Spotify are among the top companies using the platform. The engineers at LinkedIn who created Kafka, also created Confluent, the organization that focuses on it. Confluent University also provides training courses, both onsite and public, for Kafka developers, operators, and administrators. These technologies and tools are on the rise and it is only a matter of time until we see them shining above all Data Analytics organizations like stars. This article written by Joseph Macwan. He is technical writer with a keen interest in business, technology and marketing topics. He is also associated with Aegis softwares which offers Apache Spark Solutions.
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