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Big Data Services

With growing needs to analyse vast scales of data, Big Data Engineering services have emerged as a separate stream along with Data Science. The purpose of Data Science is to generate insights from data, while the purpose of Data Engineering is to manage and prepare the data for analysis.

There was a time when Data Engineering was limited to the management of data on traditional data platforms such as RDBMS, Data Warehouses (DW), Data Marts, etc. Architects designed and managed data models, data governance, master data management, and data security.

In the traditional data sets, ETL engineers managed the pipelines of data and Data Analysts generated reports using basic SQL and reporting tools. Statisticians ran models on the data sets.

Data volumes have grown exponentially in most industries over the past decade. As volumes increased, there was a need for analytics on variety of data, real-time data, and quality of data. This resulted in the need for Big Data platforms and Data Engineering for Big Data.

Big Data Solutions

Using our end-to-end Big Data consulting services and Advanced Analytics solutions delivery expertise, Indium is the ideal Big Data service provider that can help you meet your business objectives, time-to-market targets, and TCO targets.

You can derive business value from your data by choosing the right platform in a complex and constantly changing ecosystem like Big Data engineering. From the explosion of options available in the Big Data ecosystem, even Top Enterprises may find it challenging to find the right combination of tools & technologies.

Big Data Stream Processing Architecture

Streaming data is one of the key components of a big data architecture. Unbounded streams of data must be captured, processed, and analyzed. Depending on the business needs, streaming data sets can be processed & analysed in real time or near real time.

Various technologies are illustrated in the diagram above for big data stream processing. The technology choices could differ depending on factors such as cost, efficiency, open source, developer community, in-house, cloud-ready, etc. From capture to visualization, the stream processing process has four steps:

Capture - Gathering and aggregating streams (in this case logs)
Transfer - Real-time data pipeline and movement (Kafka for real-time and Flume for batch)
Process - Data warehousing and batch processing using Pentaho on Hadoop in real-time (Spark)
Visualize - Display real-time and batch data