Businesses produce a lot of data. Everything from customer feedback to sales performance and stock price influences how a company operates. But understanding what stories the data tells isn’t always easy or intuitive, which is why many businesses rely on data engineering.
Table of Contents
Table of Contents
What Is Data Engineering?
Data engineering is the process of designing and building systems that let people collect and analyze raw data from multiple sources and formats. These systems empower people to find practical applications of the data, which businesses can use to thrive.
Why Is Data Engineering Important?
Companies of all sizes have huge amounts of disparate data to comb through to answer critical business questions. Data engineering is designed to support the process, making it possible for consumers of data, such as analysts, data scientists and executives, to reliably, quickly and securely inspect all of the data available.
Data analysis is challenging because the data is managed by different technologies and stored in various structures. Yet, the tools used for analysis assume the data is managed by the same technology and stored in the same structure. This rift can cause headaches for anybody trying to answer questions about business performance.
For example, consider all of the data a brand collects about its customers:
- One system contains information about billing and shipping
- Another system maintains order history
- And other systems store customer support, behavioral information and third-party data
Together, this data provides a comprehensive view of the customer. However, these different datasets are independent, which makes answering certain questions — like what types of orders result in the highest customer support costs — very difficult.
Data engineering unifies these data sets and lets you find answers to your questions quickly and efficiently.
What Do Data Engineers Do?
Data engineering is a skill that is in increasing demand. Data engineers are the people who design the system that unifies data and can help you navigate it. Data engineers perform many different tasks including:
- Acquisition: Finding all the different data sets around the business
- Cleansing: Finding and cleaning any errors in the data
- Conversion: Giving all the data a common format
- Disambiguation: Interpreting data that could be interpreted in multiple ways
- Deduplication: Removing duplicate copies of data
Why Does Data Need Processing through Data Engineering?
Data engineers play a crucial role in designing, operating, and supporting the increasingly complex environments that power modern data analytics. Historically, data engineers have carefully crafted data warehouse schemas, with table structures and indexes designed to process queries quickly to ensure adequate performance. With the rise of data lakes, data engineers have more data to manage and deliver to downstream data consumers for analytics. Data that is stored in data lakes may be unstructured and unformatted – it needs attention from data engineers before the business can derive value from it.
Fortunately, once a data set has been fully cleaned and formatted through data engineering, it’s easier and faster to read and understand. Since businesses are creating data constantly, it’s important to find software that will automate some of these processes.
The right software stack will extract a huge amount of information and value from your data, which creates end-to-end journeys for the data known as “data pipelines.” As the information travels through the pipeline, it may be transformed, enriched and summarized several times.
Data Engineering Tools and Skills
Data engineers use many different tools to work with data. They use a specialized skill set to create end-to-end data pipelines that move data from source systems to target destinations.
Data engineers work with a variety of tools and technologies, including:
- ETL Tools: ETL (extract, transform, load) tools move data between systems. They access data, then apply rules to “transform” the data through steps that make it more suitable for analysis.
- SQL: Structured Query Language (SQL) is the standard language for querying relational databases.
- Python: Python is a general programming language. Data engineers may choose to use Python for ETL tasks.
- Cloud Data Storage: Including Amazon S3, Azure Data Lake Storage (ADLS), Google Cloud Storage, etc.
- Query Engines: Engines run queries against data to return answers. Data engineers may work with engines like Dremio Sonar, Spark, Flink, and others.
Data Engineering vs. Data Science
Data engineering and data science are two complementary skills. Data engineers help make data reliable and consistent for analysis. Data scientists need reliable data for machine learning, data exploration, and other analytical projects involving large data sets. Data scientists may rely on data engineers to find and prepare data for their analysis.
Ready to Get Started? Here Are Some Resources to Help
What Is a Data Lakehouse?
The data lakehouse is a new architecture that combines the best parts of data lakes and data warehouses. Learn more about the data lakehouse and its key advantages.read more
Simplifying Data Mesh for Self-Service Analytics on an Open Data Lakehouse
The adoption of data mesh as a decentralized data management approach has become popular in recent years, helping teams overcome challenges associated with centralized data architecture.read more