All About Data Engineering: Exploring the Key Concepts Behind Data Pipelines and Data Warehouses.

Here at CapeStart, we take pride in helping startups and Fortune 1000 companies with their data engineering needs, from constructing ETL/ELT data pipelines to developing data warehouses and data lakes.

But that also means we often get questions from clients and prospects about some aspects of the data engineering world.

So, we figured, why not write a blog post explaining some of the most frequently asked terms about data engineering?

Let’s dig in.

Data engineering

Data engineering is a collection of tools, processes, and operations that facilitate data flow from data sources to target systems and business users within an organization. Data engineering is enabled by – you guessed it – data engineers, who are responsible for constructing and maintaining the organization’s data infrastructure.  

Data engineer

As their title implies, data engineers are the IT staff that make data engineering happen. Data engineers exist to ensure an organization’s data is always of the highest quality and availability. 

Data engineers achieve this goal by building data pipelines connecting various data sources to a target system (such as a data warehouse). This includes integrating, cleaning, and structuring incoming and stored data to ensure high availability for big data analytics and other applications performed by data scientists and business users.

While most data engineers have their own unique skill set – including pipeline and database building – the job also requires a mashup of other skills drawn from software engineering and data science, including knowledge of programming languages such as Python, SQL, and Java.

Some data engineers work on the entire data lifecycle, from collection to processing, while others specialize in building and maintaining data pipelines or databases. 

Data pipeline

Picture an energy pipeline ferrying oil or natural gas cross-country from one facility to another, and you’ve got a good idea of the role of data pipelines – the difference being they’re built for data, not physical materials. Data pipelines move data between source and target locations, such as an IoT sensor and data lake, typically through a set of tools and processes built and managed by data engineers.

Data pipelines require a development environment (for building, testing, and deploying the pipeline) and tools for monitoring pipeline health (including checking for errors in data pipeline architectures). 

Intelligent data pipelines use automation as much as possible to scale the ingestion of the dramatically increasing amounts of data, data sources, and data types now being generated by many organizations.

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Extract-transform-load (ETL) and extract-load-transform (ELT) are slightly different, yet similar methods of bringing data from a source into a target system using data pipelines. 

Using an ETL workflow, data engineers first receive incoming data from data sources (extract). Next, they modify and integrate that data to standardize it and make it usable for analysts (transform). They store that data in a data warehouse or other type of data storage (load) where it’s business-ready and available for use. 

On the other hand, ELT workflows load the data into the target system (usually a cloud data warehouse) before making any transformations. Data transformations are then performed on an as-needed basis using compute power from the data warehouse itself. 

For the reasons above and others, ELT tends to facilitate better performance for lower costs because it only transforms data required by business users – not huge batches of incoming data, as with ETL. ELT helps improve development productivity, reduce infrastructure complexity, and allows IT teams to run faster data jobs.

Data warehouse

A data warehouse is a central data repository, typically a relational database, that resides either on premises or in the cloud (or both in a hybrid environment). It stores structured data.

Data warehouses (along with data engineering activities such as data integration) help remove data silos and serve as a single source of truth containing all of an organization’s data, enabling more accurate data analysis, cleaner insights, and better business decisions. 

Data warehouses consist of different layers, including an analytics layer, a semantics layer, and a data layer. They also include several basic components, including:

  • Storage. Data warehouses must be able to store all of an organization’s data and make it available to business users. Types of storage range from on-premises servers to cloud offerings such as Cloud Storage on AWS, Google Cloud Storage, and Azure Blob Storage.
  • ETL/ELT tools. See section above.
  • Metadata. For data to be searchable and meaningful for analysis queries, it must have metadata. Described by some as “data about your data”, business metadata adds context to datasets. Technical metadata indicates the structure of data and where that data is stored.
  • Data access tools. These tools allow data scientists, business users, and others to access and use the data for analysis or other applications, including query and reporting tools and data mining tools.

It’s important to note that data warehouses aren’t the same as traditional databases, which generally store data from just one source (not multiple data sources). Data warehouses typically feature fewer tables and simplified schema and queries than traditional databases, allowing for better performance across much larger datasets.

Data marts and data lakes

Data lakes use an ELT approach for integrating data and store large amounts of typically semi-structured or unstructured data from sources such as IoT devices, mobile apps, and websites. Because rules and schema don’t need to be defined in advance when capturing data for a data lake, they allow for greater flexibility and performance when dealing with unstructured data.

Data marts are essentially data warehouses on a much smaller scale (typically 100 GB or less), usually focused on one topic or line of business. Data marts can help users in enormous organizations find and use data specific to their department faster and easier, with all data still connected to the larger data warehouse (to guard against data silos).

Let CapeStart be your data engineering and data warehousing partner

CapeStart’s data engineering, data science, big data and data warehousing teams work with organizations large and small every day to guide their data engineering efforts – from custom ETL/ELT pipelines, to data warehouse creation and migration. Contact us to set up a brief discovery call with one of our technical experts.

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