Future of Data Engineering

Businesses today make informed decisions backed by data thanks to the increasing access to enterprise-wide data because of Internet of Things (IoT) devices. The insights the data can provide can improve speed, flexibility and quality while lowering operational costs. No wonder then that the global Big Data market size is expected to grow at a Compound Annual Growth Rate (CAGR) of 10.6% from USD 138.9 billion in 2020 to USD 229.4 billion by 2025, according to a Marketsandmarkets.com forecast.

However, as a Gartner analysis points out, all that data being generated can be of any use only if the right data is provided to the right people at the right time. Today, in the world of digital transformation, it means ‘Right Now!’ Businesses need information as it is unraveling and not in some distant future. They need to take instantaneous decisions, respond to customer queries and needs, solve supply chain problems, handle logistics issues as they are happening. Any delay can mean missed opportunities, costing the business in millions. It can impact revenues and growth prospects.

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This instant need for insights requires data management to keep pace with the changing requirements and manage data in innovative ways to meet the real-time data needs. The role of data engineering is becoming even more critical now and the processes and tools are undergoing a change to provide clean, trustworthy and quality to business users across the enterprise to make informed decisions at the speed of light.

Evolving Role of Data Engineering

In every organization, data flows through multiple sources and in multiple formats. The data is stored in different databases, creating silos. Data access becomes a challenge, hiding vital information from the decision-makers that could change the course of their business. Moreover, data needs to be cleaned, transformed, processed, summarized, enriched and stored securely, all as it is flowing into the organization.

The role of data engineering is now expanding. Data engineers do all that they were doing earlier to provide data to data analysts, scientists and business leaders. But they also need to be able to match the pace of the requirements of these users. It is no more about creating metadata in a leisurely way but creating data pipelines right from acquisition to encoding, instantly for current and future needs.

Real time creation of data pipeline requires the following four steps:

  • Capture – Collect and aggregate streams (Using Flume)
  • Transfer – Using Kafka for real-time and Flume for batch – Flume
  • Process – Real-time data is processed using Spark and batch processing is performed on Hadoop using Pentaho
  • Visualize – Visualization of both real-time and batch processed data

Meeting Real-Time Needs

As businesses become future-ready, the approach to data engineering is also undergoing a transformation. The function is fast-changing where batch ETL is being replaced by database streaming, with traditional ETL functions occurring in real-time. The connection between data sources and the data warehouse is strengthening and with smart tools self-service analytics becoming the need of the hour. Data science functions are also getting automated to be able to predict future trends quickly and course correct current strategies to meet those needs.

Another trend that is emerging is that of hybrid data architectures where on-premise and cloud environments are co-existing, with data engineering now having to deal with data from both these sources.

Data-as-it-is is another trend that is changing the way storing of data is being impacted – is becoming nearly irrelevant due to the growing popularity of real-time data processing. While this has made data access simpler, data processing has become more difficult.

All these trends have expanded the role of the data engineer. However, where are the data engineers to meet this demand?

A Databridge report suggests that though Big Data is exciting and suggests many possibilities for businesses, in reality, lack of a skilled workforce and complexity in insights extraction are major hurdles to it being leveraged and its potential explored to the optimum. Since 2012, the job postings for data engineers have gone up 400%, and in the last one year, they’ve almost doubled.

Especially in the last two years, there is more digital transformation of businesses because of which there has been a tremendous increase in the data being generated. This is only going to grow as more businesses opt for digital transformation and experience an explosion in data in their organizations.

Indium as Data Engineering Partner

Businesses will need partners with experience in Big Data and Data engineering to be able to handle their data processing in real-time while keeping their costs low.

A partner such as Indium Software, with more than two decades of experience in cutting edge technologies, can be an ideal fit. Our team has expertise in data engineering, handling data processing in real-time and the latest technologies such as Python, SQL, NoSQL, MapReduce, HIVE, PIG, Apache Spark, Kafka.

Indium offers Big Data technology expertise with rich delivery experience to enable our clients to leverage Big Data and business analysis even on traditional platforms such as enterprise data warehouse, BI, etc.

A well-thought-out reference architecture for Big Data solutions that is flexible, scalable and robust and using standard frameworks for executing these services, Indium also helps organizations improve efficiencies, reduce TCO and lower risk with commercial solutions. Indiums offers consulting, implementation, on-going maintenance and managed services to derive actionable insights and make quicker and informed decisions.

A leading, 130-year-old Italian bank with more than 300 branches spread across Italy,

Ireland, India and Romania, offering a wide range of customized financial and

banking products wanted a scalable real-time solution to analyze data from all

workloads and provide operational intelligence, to proactively reduce server downtime. They wanted their server logs mined in real-time for faster troubleshooting, RCA and to prevent server performance issues.

Indium used Apache Flume daemons to fetch and push server logs to Apache Storm through Kafka messaging queue. The data processing was done in real-time in Apache Storm and the

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processed data was loaded into Hbase NoSQL database. D3.js visualization was built by the bank on top of this processed data. The raw data from Apache Storm was pushed to Apache SOLR to enable Admins perform text searches and gain insights, also in real-time.

The entire application was built from the ground up in less than two weeks and tested with production grade data and tuned for performance to get real-time insights from server logs generated in 420 servers.

Indium can help you get future-ready and make informed decisions based on real-time data. Contact us now to find out how.