What is Data Engineering?
Data Engineering is the process of designing, building, and maintaining data systems and architectures that allow businesses to make sense and leverage their data. Data Engineering is responsible for ensuring data is transformed from raw, unstructured sources into an accessible format usable by data scientists, analysts, and other decision-makers.
In addition to making data accessible, data engineers create raw data analyses to provide predictive models and show trends for the short- and long-term. Without data engineering, it would be impossible to make sense of the huge amounts of data that are available to businesses.
The Data Pipeline
Data engineers play a critical role in each phase of the data pipeline, which involves moving data from source to use. There are four key phases of the data pipeline that data engineers manage:
1. Ingestion - This is the process of gathering data from various sources, such as databases, external APIs, and real-time systems. Depending on the size and diversity of these sources, data ingestion can range from simple to large-scale operations involving batch or streaming methods.
2. Processing - During this phase, the raw data is transformed and cleaned, ensuring it meets the organization’s requirements for analysis. For large datasets, distributed computing frameworks like Apache Spark or Hadoop are often used to process data efficiently at scale.
3. Storing - After processing, the data is stored in databases, data lakes, or data warehouses. A robust data storage strategy ensures that data is easily accessible and can be queried quickly, whether the storage is on-premises or in the cloud.
4. Access - Once data is ingested, processed, and stored, it becomes available for use by analysts, data scientists, and business teams. Data access needs to be well-governed and secure to ensure the right people can leverage the data without compromising its integrity or security.
Why is Data Engineering important?
Without a well-defined data engineering strategy, a company's data becomes chaotic, inconsistent, and ultimately unusable. Data engineering provides the foundational infrastructure that enables businesses to turn their raw data into actionable insights. With reliable data pipelines and systems, businesses can make faster, data-driven decisions, respond to trends in real time, and efficiently scale their operations. Data engineering is not just a technical necessity; it’s a competitive advantage that drives innovation and efficiency.
How Precisely helps data engineers
Precisely offers data integration and data quality solutions that support data engineers at every stage of the data pipeline. From ingesting data from multiple sources, including legacy systems like mainframes, to ensuring the quality of data at scale through entity resolution and data cleansing, Precisely helps build pipelines that deliver trusted, accurate data.
Whether setting up real-time streaming data or preparing large datasets for machine learning or advanced analytics, the tools provided by Precisely enable effective management of data infrastructure, ensuring insights that drive real business value. Visit our website to learn more about the Precisely Data Integrity Suite.