Future of Data Engineering Solutions

Businesses increasingly make data-driven choices due to increased access to enterprise-wide data via Internet of Things (IoT) devices. Data insights can increase speed, flexibility, and quality while cutting operating expenses. According to Marketsandmarkets.com, the worldwide Big Data market is predicted to increase at a Compound Annual Growth Rate (CAGR) of 10.6 percent from USD 138.9 billion in 2020 to USD 229.4 billion by 2025. 

However, as a Gartner report points out, all of that data may be useful only if the appropriate information is sent to the right people at the right time. It now implies ‘Right Now!’ in the field of digital change. Businesses want knowledge as it emerges, not in the distant future. They must make quick choices, respond to client inquiries and requests, manage supply chain concerns, and handle logistics challenges as they arise. Any delay can result in missed chances, costing the company millions of dollars. It has the potential to influence revenue and growth prospects.

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 solutions are becoming even more critical now, and the processes and tools are changing to provide clean, trustworthiness, and quality to business users across the enterprise to make informed decisions at the speed of light.

Evolving Role of Data Engineering Solutions

Data travels through numerous sources and forms in every company. Data is kept in several databases, resulting in silos. Access to data becomes difficult, concealing vital information from decision-makers that might influence their organization’s path. Furthermore, data must be cleansed, converted, processed, summarized, enhanced, and securely stored as it enters the organization.

The function of data engineering solutions are becoming more prominent. Data engineers continue to perform what they were doing previously to offer data to data analysts, scientists, and business executives. However, they must also be able to keep up with the demands of these consumers. It is no longer about producing metadata in a leisurely manner but about immediately constructing data pipelines from capture to encoding for present and future demands. Real-time creation of data pipeline requires the following four steps:

• Capture – Gather and aggregate streams (using Flume) 

 • Transfer – Kafka for real-time and Flume for batch – Flume

 • Process – Real-time data is processed using Spark, while batch processing is handled on Hadoop with Pentaho. 

 • Visualize – Both real-time and batch-processed data are visualized.

Meeting Real-Time Needs

As companies become more future-ready, the approach of data engineering solutions is changing. The function is rapidly evolving, with batch ETL replaced by database streaming and classic ETL activities taking place in real-time. The link between data sources and the data warehouse is strengthening, and with smart technologies, self-service analytics is becoming more important. Data science functions are also automated to swiftly identify future trends and course correct present efforts to suit those demands.

Another rising trend is hybrid data architectures, in which on-premise and cloud systems coexist, with data engineering solutions now needing to deal with data from both sources.

Data-as-it-is is another trend that is changing the way data storage is impacted — it is quickly becoming obsolete due to the rising popularity of real-time data processing. While this has simplified data access, it has made data processing more challenging.

All these developments have broadened the function of the data engineer. Where are the data engineers, though, to satisfy this demand?

According to a Databridge analysis, while Big Data is fascinating and implies many possibilities for organizations, a shortage of qualified labor and complexity in insights extraction are considerable barriers to it being used and its potential fully explored. Since 2012, job posts for data engineers have increased 400%, and they have nearly quadrupled in the previous year. 

Particularly in the previous two years, firms have had a greater digital transformation, resulting in a massive rise in data generation. This will only increase as more firms choose for digital transformation and face a data explosion in their enterprises. Clairvoyant can help you get future-ready and make informed decisions based on real-time data. Contact us now to find out how.