Businesses produce a lot of data. Everything from customer feedback to sales performance and stock price influences a company’s daily operations. However, understanding what stories the data tells isn’t always easy or intuitive, which is why many businesses rely on data engineering.

What is Data Engineering?

Data engineering is designing and building systems that let people collect and analyze raw data from multiple sources and formats. These systems empower people to find practical data applications that businesses can use to thrive.

Why Is Data Engineering Important?

Companies of all sizes have vast amounts of disparate data to comb through to answer critical business questions. Data engineering is designed to support the process, making it possible for data consumers, such as analysts, data scientists, and executives, to inspect all available data reliably, quickly, and securely.

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 answering questions about business performance.

For example, consider all the data a business collects about its customers:

• One system contains information about billing and shipping
• Another system maintains order history
• 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 specific 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 standard format
• Disambiguation: Interpreting data that could be interpreted in multiple ways
• Deduplication: Removing duplicate copies of data

Once done, data may be stored in a central repository such as a data warehouse, data lake, or data lakehouse.

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 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 thoroughly cleaned and formatted through data engineering, it’s easier and faster to read, understand, and consume. Since businesses are constantly creating data, it’s important to use software to automate the collection and storing of the data.

The right software stack, such as Oracle GoldenGate, Quest Streams, or CDC-related tools, will extract a vast 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:

• CDC Tools: Change Data Capture tools move data between systems. These tools include Oracle GoldenGate and Quest Streams, among many others.
• ELT Tools: Extract, Load, Transform tools “extract” data, “load” it data, if needed, between systems, then “transform” 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 ELT tasks.
• Cloud Data Storage: This includes Oracle (OCI) Storage Buckets, 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 Spark, Flink, etc.

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.

Data Engineering with RheoData

RheoData Data Engineers help you break down the data silos and simplify your organizations data management.  Providing a single, unified access point for all enterprise data for business intelligence (BI), ad-hoc reporting, and machine learning (ML), and artificial intelligence (AI) use cases.  Enabling you to achieve higher ROI on your data and infrastructure modernization journey.

Ready to get started? Contact us today!