Different Big Data Platforms

Big data technology platforms play a vital role in providing more accurate analysis, which may lead to more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business. Following are the list of Big Data platforms that are familiar to the industry,

• Apache Hadoop
• Apache Flume
• Impala
• Apache Pig
• Apache Tajo
• Apache Spark
• Apache Storm
• Apache Spark SQL
• Avro
• Couch DB
• Cassandra
• Cognos
• Google Charts
• Hcatalog
• Apache Hive
• High Charts
• Apache Hbase
• JFree Chart
• Apache Kafka
• MapReduce
• Apache Mahout
• Pentaho Reporting
• Qlikview
• R Programming
• SAS
• SQOOP
• Statistics
• Teradata
• Tableau
• Apache Zookeeper

To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in real-time and can protect data privacy and security.

There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology:

Operational Big Data

This includes systems like MongoDB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored.

NoSQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement.

Some NoSQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure.

Analytical Big Data

This includes systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data.

MapReduce provides a new method of analyzing data that is complementary to the capabilities provided by SQL and a system based on MapReduce that can be scaled up from single servers to thousands of high and low-end machines.

These two classes of technology are complementary and frequently deployed together.

Operational vs. Analytical Systems

Big Data Challenges

The major challenges associated with big data are as follows:

• Capturing data
• Curation
• Storage
• Searching
• Sharing
• Transfer
• Analysis
• Presentation

To fulfill the above challenges, organizations normally take the help of enterprise servers.