Top 7 Technologies to Unleash the Full Potential of Big Data

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When I was wondering “how big is ‘Big Data’?”, I stumbled across an excellent description saying, “A Big Data can be as big as a million of exabytes (1,024 petabytes) or a bazillion of petabytes (1,024 terabytes) containing billions and trillions of records from people worldwide”. And that’s amazing!

Big data is massive and exploding!! Hundreds of companies worldwide are springing up with new projects to extort the full potential of Big Data – that of rapid extraction, loading, transformation, search, analysis and share massive data sets.

Here we go top 7 open source technologies to bring out the best of Big Data that you should start adopting today.

Apache Hive 2.1: If you want your applications to run 100 times faster, Apache is your solution. Apache Hive is Hadoop’s SQL solution. The latest release features performance enhancement keeping Hive as the only solution for SQL on petabytes of data over clusters of thousands of nodes.

Hadoop: One of the most popular MapReduce platforms, Hadoop is a robust enterprise-ready solution to run Big Data servers and applications. For this, you need YARN and HDFS for your primary data store.

Spark: Yet another no—brainer, Spark offers easy-to-use technologies for all Big Data Languages. It is a vast ecosystem that is growing rapidly providing easy batching/micro-batching/SQL support.

Pheonix: An SQL skin on Apache HBase, Pheonix is ideal to support Big Data use cases. It replaces regular HBase client APIs with standard JDBC APIs to insert data, create the table and send queries to HBase Data. It reduces the amount of code, allows transparent performance optimisation to the user, integrates and leverages the power of several other tools.

Zeppelin: It calls itself a web-based notebook empowering interactive data analytics. You can just plug in data/language processing back end into Zeppelin that supports interpreters like Python, Apache Spark, JDBC, Shell and Markdown.

Kafka: Kafka is a fast, durable, scalable and fault-tolerant subscribe and public system. It often replaces message brokers like AMOP and JMS as it features higher throughput, replication and reliability. It is combined with Apache HBase, Apache Storm and Apache Spark for streaming of data and real-time analysis.

NiFi: NiFi maximises the value of data-in-motion. It is designed and built to automate the data flow between systems and create secure data ingestion. Two key roles of NiFi are:
• Accelerate Data Collection and enhances movement for ROI on Big Data
• Collect, secure and transport data from IoT.

Idexcel Big Data Services is focused on dealing effectively with technologies & tools that enable efficient management of Big Data Volume, Diversity and Velocity. With massive and active client engagement spanning several verticals, we help businesses in building data analytics decision within the organisation.

That said, would you like to be another name enlisted on our happy customer directory?

Budget Strategies for Maximizing Big Data – Idexcel Big Data Roundup

1. Budget Strategies for Maximizing Big Data

Big data doesn’t necessarily come with a big pricetag. Here, an expert offers his tips for using big data on a small budget.

Got an operational problem?

Big Data will solve it! Marketing ills? Ask Big Data! Those two words have become a catchall — but data-crunching services tend to chase after enterprise-level businesses, making them out of reach for most small businesses. (Google Analytics Premium, for example, starts at $150,000 a year.) Don’t worry: Martineau says that with a strategic approach to Big Data, anyone can afford it. Continue reading…

2. Hadoop Overview: A Big Data Toolkit

Big Data isn’t new. Forbes traces the origins back to the “information explosion” concept first identified in 1941. The challenge has been to develop practical methods for dealing with the 3Vs: Volume, Variety, and Velocity. Without tools to support and simplify the manipulation and analysis of large data sets, the ability to use that data to generate knowledge was limited. The current interest and growth in Big Data, Data Science, and Analytics is largely because the tools for working with Big Data have finally arrived. Hadoop is an important piece of any enterprise’s Big Data plan. Continue reading…

3. Mendix Low-Code Mobile Dev Platform Connects IoT, Big Data and Machine Learning

Mendix today announced a new version of its low-code mobile development platform, designed to help developers build “Smart Apps” with connectors to accommodate emerging trends such as the Internet of Things (IoT), Big Data and machine learning (ML). Continue reading…

4. Marketing Technology Vendors Offer Big Success with Big Data

As you’re probably already aware, the marketing technology vendor landscape is amazingly vast. Scott Brinker, editor of chiefmartec.com, shares that in 2016 there are at least 3,874 vendors hawking their wares in the marketing technology space. And every single one of them uses data.
To that end, following are four examples of marketing technology vendors using big data effectively: Continue reading…

5. Role of Risk Audits: How the Cloud & Big Data have Changed Them

The role of auditors has been changing rapidly over the past decade. Big data is allowing them to make higher quality decisions. However, their job is also becoming more complicated, so future financial auditors will need a strong background in IT. Continue reading…