Big Data stack has emerged from the shadows in a big way over the last couple of years. Much of its success has emanated from the open source tools that have transformed aspects ranging from Data Ingestion, Data Storage, Data Access to Visualization and Adhoc Reporting through tools like Sqoop, Flume, HDFS, HBase, Cassandra,Hypertable, CouchDB,MongoDB, YARN, MapReduce, Spark, Flink, Storm, Sarnia to BIRT and JasperReport. Some of the most important stack in Big Data have emanated from Yahoo and Google namely Hadoop and Mapreduce. Read on to understand how open source is empowering data science and enabling big data in all business sectors.
The concept of big data as well as open source, both have existed independently for a very long time. However, the challenge of data management is relatively new. The amount of data being generated by social media feeds, machine data and IoT devices are constantly growing, making it increasingly difficult to derive insights from it. Making use of open source solutions in big data has been found to streamline almost every stage of data processing.
For data management platforms, proprietary software is no longer a solution that suffices. To efficiently handle the massive amount of data flowing in from multiple channels, non-relational databases are being used not only for creating web applications that collect data, but also for building databases which facilitate data management.
When it comes to collecting, managing and analyzing data there are two basic advantages of open source that enable it to provide reliable data processing solutions. Opensource adds value by reducing development effort, offering developers the core technology stack and freeing them to work on a more complex task, this approach helps corporations deploy data analytics faster and accelerate the ROI of there Big Data investment.
Apache foundation has been one of the main services that are leveraged by organization to deploy, integrate and work with large amounts of structured and unstructured data but other than Apache foundation there are a number of other interesting solutions available as well especially with cloud now increasingly becoming a foundation stone of Big Data deployment and we now have some other options well. Let’s explore
The way Google looked at search, one of its first challenges was to understand and address how to index and rank a large volume of data that was there on the internet. MapReduce came into the picture to solve this use case and manage the processing of a large volume of data on commodity servers. MapReduce is a framework programming structure for processing and analyzing large data sets. MapReduce functions are automatically parallel distributed on a cluster of servers.
Apache Spark is widely utilized for big data processing with its built-in capabilities for on the go data streaming, SQL, and machine learning this is a tool of choice for a large number of organizations. Spark can seamlessly run in-memory within databases like MongoDB and enables interactive streaming analytics on the fly. When corporations need to make a forecast in real time in use cases like pricing and programmatic advertising Apache Spark is the tool of choice as unlike batch processing, vast amounts of historical data can be analyzed with live data to make real-time decisions.
TensorFlow is the developer tool of choice for corporations looking at deploying machine learning. TensorFlow has been designed for large-scale distributed modeling and prediction, but it also gives developers the option of learning new machine models and system-level optimizations. One of the key advantages that are there with Tensorflow is that is intuitive, well documented and has a fast growing community of developers.
GraphQL is a query language that was first built and used inside Facebook as its mobile apps became more complex and started experiencing performance issues. GraphQL is essentially a query language for APIs and provides a comprehensive description of the data in the API. GraphQL is very similar to RESTAPI in the sense that it is used to get data from any backend service.
Apache Flume is a service that is used for collecting, aggregating, and distributing large amounts of streaming data into Hadoop Distributed File System. One of the scenarios that could be used to describe a Flume deployment. Imagine an organization using a large number of servers to run various services. This scenario creates a large number of log files that need to be analyzed and processed using Hadoop. To dump all the logs in the Hadoop in a manner that they maintain reliability, scalability, extensibility Flume is one of the tools that is the tool of choice.
These are just a few examples of the ways open source is changing the big data landscape. Future is exciting and will continue to foster new innovation in this domain. Within Dataeaze we continue innovating and exploring this domain and we will continue to partner with innovative corporations to bringing exciting big data solutions to corporations across the globe.