It feels like a Global Warming. The traditional Business Analytics icebergs are melting and Data Analytics Ocean is getting much bigger day by day. But the difference is, the first said comparison is really dangerous to our earth and the latter one is the best thing happened in the industry.

When I googled on the latest Big data topics (shhhhhh… Don’t tell this to my boss…) I got nearly 100’s and 1000’s of search results but somehow I managed to scrape those and chose the trendiest topics in Big Data.

  1. Big Data Infrastructure:

Amazon Web Service a collection of remote computing services, also called web services, make up a computing platform offered by

Cloudera is the leader in next generation data management. In addition, Cloudera is the leading innovator in and largest contributor to the open source Apache Hadoop ecosystem.

Altiscale developed a purpose-built, petabyte-scale infrastructure that delivers Apache Hadoop as a cloud service.

BlueData Software platform makes it easier, faster, and more cost-effective to deploy Hadoop and Big Data infrastructure on-premises.

Hortonworks Data Platform (HDP), is an enterprise-grade data management platform that enables a centralized architecture for running batch, interactive and real-time applications simultaneously across a shared dataset.

IBM big data solutions can capture, manage and analyze huge volumes of structured and unstructured data to improve business insights.

MapR Apache Hadoop distribution claims to provide full data protection, no single points of failure, improved performance, and dramatic ease of use advantages.

Pepperdata software is easy to install and runs on top of all Hadoop distributions without modifying the existing scheduler, workflow, and job submission process.

Snowflake Computing can safely store, transform and analyze business data, making it easy for everyone to quickly gain insight.

Syncsort provides enterprise software that allows organizations to collect, integrate, sort and distribute more data in less time, with fewer resources and lower costs.

Teradata is engineered for all new data types, offering integrated analytics and revolutionary ways of analyzing data.

Treasure Data helps to collect, analyze, and act on the data safely and efficiently.

Pivotal is compatible with distributions of Open Data Platform (ODP) versions of Hadoop. All components are distributions of open source projects or are in the process of becoming open source projects.

Qubole gives you the flexibility to choose your cloud(s). Qubole’s auto-scaling provides unlimited scalability, automatically adding or removing compute resources to reflect usage.

  1. Open Source Tools:

Apache Avro is a data serialization system.

Apache Chukwa is an open source data collection system for monitoring large distributed systems.

Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.

Apache Hadoop project develops open-source software for reliable, scalable, distributed computing.

HPCC System is a massive parallel-processing computing platform that solves Big Data problems. The platform is Open Source!

Apache Lucene is a high-performance, full-featured text search engine library written entirely in Java.

Apache Oozie is a workflow scheduler system to manage Apache Hadoop jobs.

Apache Solr is the popular, blazing-fast, open source enterprise search platform built on Apache Lucene.

Apache Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured data stores such as relational databases.

Apache Storm is a free and open source distributed real-time computation system.

Terracotta provides leading in-memory data management and Big Data solutions for the enterprise, including BigMemory, Universal Messaging, and more.

Apache Zookeeper is an effort to develop and maintain an open-source server which enables highly reliable distributed coordination.

Apache Zeppelin A web-based notebook that enables interactive data analytics.

Apache Tez is aimed at building an application framework which allows for a complex directed-acyclic-graph of tasks for processing data.

Apache Kafka is publish-subscribe messaging rethought as a distributed commit log.

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments.

  1. Big Data Services:

Talend Open studio  products and open architecture create unmatched flexibility so you can solve integration challenges your way.

Palantir Big Data builds data fusion platforms for integrating, managing, and securing any kind of data, at massive scale. On top of these platforms, they layer applications for fully interactive, human-driven, machine-assisted analysis.

Google BigQuery is Google’s fully managed, NoOps, data analytics service.

Pentaho Big Data Analytics Within a single platform, Pentaho provides big data analytics tools to extract, prepare and blend your data, plus the visualizations and analytics.

Cisco Big Data provides unique solutions to connect all kinds of data wherever it is, bringing compute and analytics from the data center to the edge for faster insights and action.

Splunk Big Data Analytics  can collect and index any machine data in real time and it is made easier to search, explore, navigate, analyze and visualize data from one place.

Intel big Data develops a practical strategy for integrating big data analytics into the business.

Redhat Big Data JBoss Data Virtualization can integrate with Hadoop through Hive and provide users easy access to data.

Informatica Big Data  products simplify Hadoop complexity by providing a single, scalable platform that works with traditional data warehouses, cloud-based data, and new types of data from social media and sensor devices.

  1. Big Data Products/platform:

Tableau 9.0  gives instant visual feedback as experimenting with data.

Qlik Sense 2.0. is a leader in data discovery delivering intuitive solutions for self-service data visualization and guided analytics.

Snowflake Elastic Data Warehouse A data warehouse that is more flexible, scalable, and easy to use than anything else available.

Domo brings the business and all its data together in one intuitive platform.

Attivio 5.0 unlocks the business value trapped in text-based sources of information by making it easy to analyze dark data – giving you a more complete view so you can act with certainty.

Data Torrent RTS 3, which runs on Amazon EMR, is powering PubMatic’s real-time Ad analytics platform enabling publishers to drive the highest value for their digital media assets.

MongoDB 3.0 features performance and scalability enhancements that place MongoDB at the forefront of the database market as the standard DBMS for modern applications.

CouchBase N1Ql is the first query language to leverage the complete flexibility of JSON with the full power of SQL.

HP Vertica Excavator is the latest version of HP Vertica enables organizations to quickly ingest and analyze high-speed streaming data, from various sources, including Internet of Things applications, and provides enhanced SQL analytics and performance to Hadoop.

  1. Big Data Visualization Tools:

1010Data is a complete cloud based big data visualization and analytics solution (an Analytics Platform as a Service – APaaS).

Datameer is an end-to-end big data discovery and analytics platform, purpose built for Hadoop.

GoodData is a cloud SaaS analytics platform which connects to a large number of data sources – big data, databases, online apps, social data, and so on.

Lumify is an open source project to create a big data fusion, analysis, and visualization platform designed for anyone to use.

Spotfire supports easy to build data visualization, text analytics, predictive analytics and statistical analysis.

  1. Big Data Solutions:

Sqrrl unifies several Big Data approaches into a single platform, including Hadoop, linked data analysis, machine learning, Data-Centric Security, and advanced visualization.

Dell helps companies of all sizes achieve insights to make better, faster decisions, enhance customer experiences and improve their IT economics.

  1. Big Data Analytics Applications:
  • Deep Learning with Analytics:

When Cortana, reminds me to wish my friend on her birthday while she calls, Google customizes the search result according to my location and when amazon suggests me an accessories to go with the previously bought Capris, these are nothing but an example for “Deep Learning”. Deep learning is part of a broader family of machine learning methods based on learning representations of data.

  • Analytics in Natural Language Processing:

Natural Language processing can analyze both handwriting and human voices. This can bring an enormous change in our day to day life. We can use NLP in identifying and analyzing doctor’s handwriting and can store that data for future references. This can also be used in sales call, where we could calculate the probability of whether this is a success call or not using Voice Analytics.

According to IDC experts, 40 zettabytes of data will be in existence by 2020, which is going to be a whole new level of “Big Data”, and it would be almost impossible to handle those huge monstrous data with such a few tools that are now available in the market. So in the upcoming years, the tools and analytics software will surely be emerging according to the Big Data market.

So…, which is the most fascinating Big Data Analytics topic(s) according to you? And if you have any other topics to add to the list, please write to us about your ideas (we would love to hear from you) and Stay Tuned.

Date Published: August 25, 2015

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