What is Data Engineering?
Data engineering is the complex task of making raw data usable to data scientists and groups within an organization. Data engineering encompasses numerous specialties of data science.
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
There are four key phases of the data pipeline that data engineering directly deals with:
- Ingestion - This is the task of gathering data. Depending on the number of data sources, , this task can be focused or large-scale.
- Processing - During this phase, ingested data is sorted to achieve a specific set of data to analyze. For large data sets, this is commonly done using a distributed computing platform for scalability.
- Storing - This takes the results of the processing and saves the data for fast and easy retrieval. The effectiveness of this phase relies on a sound database management system - which can be on premise or in the cloud
- Access - Once in place, the data is available to users with access.
Why is Data Engineering important?
If your company lacks a fundamental data engineering strategy, the data that is collected is essentially useless. Data engineering is a vital aspect of company growth, network interactions, and predicting future trends.
How Precisely helps data engineers
Tackling the challenge of designing a machine learning model and putting it into production is the key to getting value back from your big data. But it's also typically the roadblock that stops many promising machine learning projects.
After the data scientists have done their part, engineering robust production data pipelines has its own set of challenges. Precisely software helps the data engineer every step of the way.
Precisely's data integration products can help build real-time streaming data pipelines from multiple sources across the enterprise, including legacy systems such as mainframes. Use Precisely's data quality solutions to provide entity resolution at scale, to cleanse your big data -- for insights you can trust.
For more information, check out our series of 15-minute webcasts on engineering machine learning data pipelines: