Big Data Technologies

"Big Data" refers to data that, for some reason, cannot be managed appropriately by traditional systems. It represents the enormous amounts of data and diversity of data that organizations can ask questions about their customers and their business that they've never asked before.

Many organizations use the data they obtain to gain a more loyal customer base with the analysis they make from various data sources to provide a better customer experience. Cloudera, the provider of software and services to meet the analysis needs of institutions, offers a reliable platform to Hadoop institutions.

Pentaho is a modern data integration, orchestration, and business analytics platform. This comprehensive software is used with minimal IT support to enable real-time visual data analysis and predictive modeling to drive better business results, extract maximum value from data, and present, access, prepare, mix, and analyze the best data for business and IT users.

Big Data has gained more ground with the increase in the devices used and the need to process the data produced by these devices. Since the data format is different and comes from various sources, it has a structure that cannot be processed with traditional systems. Big data technologies are used to process and analyze big data and reveal accurate business results and predictions. Companies can provide better service and solutions to their customers with extensive data analysis.

The Big Data world contains many open source software. Gantek supports many technologies, such as Cloudera, Spark, Kafka, Pentaho, MapReduce, ZooKeeper, and Confluent, which have become indispensable in the industry.

Cloudera, Apache Hadoop software provides cost and performance advantages for storing and processing big data. With the differentiation of data format, NoSQL solutions also serve different purposes according to specific data types.

The Pentaho Business Analytic Platform enables the integration of big data technologies, data manipulation, orchestration of machine learning algorithms, and data visualization. In this way, with a single platform, traditional and big data worlds can be blended as 'in-memory' and 'real-time,' machine learning algorithms can be run, and analyses can be made to reveal accurate business outputs/predictions from the data.

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