Market Size of Big Data Engineering Services Industry
| Study Period | 2019 - 2029 |
| Market Size (2024) | USD 79.34 Billion |
| Market Size (2029) | USD 162.22 Billion |
| CAGR (2024 - 2029) | 15.38 % |
| Fastest Growing Market | Asia Pacific |
| Largest Market | Asia Pacific |
Major Players
*Disclaimer: Major Players sorted in no particular order |
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Big Data Engineering Services Market Analysis
The Big Data Engineering Services Market size is estimated at USD 79.34 billion in 2024, and is expected to reach USD 162.22 billion by 2029, growing at a CAGR of 15.38% during the forecast period (2024-2029).
Application programming interfaces are necessary for data integration and engineering. Data engineers use specialized tools, procedures, and equipment to prepare and analyze data for later analysis. Making data valuable begins with data engineering.
- The COVID-19 pandemic has provided data analysts with fantastic opportunities to research global Big Data patterns. Any organization would benefit from this strategic approach to data engineering, but it is crucial for businesses in sectors like banking that aim to develop gradually while simultaneously innovating quickly.
- The financial industry is quickly changing and providing new consumer products and services. The Banking Industry would significantly impact Data Engineering Market. The National Australia Bank and Amazon Web Services partnership have grown. According to the bank, 70% of its programs have now been migrated to the cloud, and it just became the first significant Australian bank to convert its online business banking platform.
- The amount of data used in healthcare is growing quickly. Electronic health records are the most prevalent significant data source in the healthcare industry. In contrast to the past, when this information was stored in handwritten files, medical researchers can now create prediction models thanks to the enormous data created by EHRs and powerful analytics techniques like machine learning.
- Not comprehending the needs of a specific user group is difficult for a data engineering project. The endless influx of data and dealing with value inconsistencies can quickly become overwhelming. Establishing a thorough data management strategy with a data governance plan is one potential response to this data engineering challange.
