According to a recent report published by ResearchandMarkets.com, the data engineering market is expected to witness a growth at a CAGR of 16.3% in the period from 2021 to 2026. Data engineering according to Gartner is “the methodology of making the appropriate data accessible and available to the data consumers (which include data scientists, data analysts, business analytics and business users).”
Data storage and processing used to be the primary challenges, not a very long time back. The emergence of Cloud have transformed both storing and processing of data into assets or commodities. This extensively helped the teams to concentrate on bigger problems such as efficiently handling of metadata management, integration of various data systems, tracking and realising a high data quality. Today, as more organisations look at revamp their analytics environments, their use of data engineering to drive better business insights is on the rise.
To better understand, data engineering comprises of the task of making raw data usable to data scientists and groups in an organisation. It also includes a number of specialities of data science. Data engineering also creates a analyses of providing predictive models and exhibit short- and long-term trends.
Data engineers lend a helping hand to data scientists and data analysts find the right data, make it accessible in their environment, ensure the data is credible and that sensitive data is hidden, functionalize data engineering pipelines and also ensure lesser time is spent on the preparation of data.
Enterprises must choose a platform and AI-driven approach for end-to-end data engineering instead of stitching together piecemeal solutions. The platform must also boost technologies like cloud, Spark, serverless, and Kafka that have led to the emergence of data engineering.
- Find the right dataset with an intelligent data catalogue.
- Bring the appropriate data into your data lake or ML environment with mass ingestion.
- Functionalize your data pipelines with enterprise data integration.
- Process real-time data at scale with AI-powered stream processing.
- Shield confidential information with intelligent data masking.
- Safeguard trusted data to be available for insights with intelligent data quality.
- Streamline data prep and enable collaboration with enterprise-class data preparation.
HOW CAN WE HELP?
In this article, we looked at the top insights in the data engineering market as well as some of the existing data engineering challenges. Now, let us see how VirtueTech can take care of and add value to your data engineering needs.
- Our solution can help your business to strengthen your ‘Data As a Service’ capability and transform big data pipelines into robust systems prepared for business analytics.
- Our team is committed to provide you the access to the right format of data at the right time across your enterprise.
- Our solutions not just only help to accelerate the integration of analytics into your business process, but also reduce the time and complexity, as well as ensure compliance with security and privacy requirements. This can assure that your business adapts effortlessly to new technological changes.
- We also offers an integrated approach to collect, store, govern, and analyse data at any scale for driving a successful data engineering initiative in your organization.
CONCLUSION
The world today is investing hugely on data science to draw maximal insights and leverage its benefits. Therefore, what we must always remember is optimizing and improving the data science processes. Data engineering services facilitate existing data science solutions and adds value to the business by saving costs and time.
Write to us at contact.us@virtuetechinc.com sharing your thoughts on data engineering and we will help you find out the scope of data engineering in your business.
