🐣 It’s created in a variety of platforms, and data engineers rarely have control over any of them. In the first stage of the lifecycle, data is born. Source: Fundamentals of Data Engineering by Joe Reis, Matt Housley Generation Essentially, the lifecycle breaks the data pipeline we all know and love into its critical pieces to see where data starts and how we maintain quality as it moves to the end. In “Fundamentals of Data Engineering,” Matt and his co-author, Joe Reis, introduced the lifecycle concept. “That’s what a ‘holistic view of data’ means.” The data engineering lifecycle “We need to think about where data starts, where it ends up, how it flows through the pipeline, and how we maintain quality as it moves,” Matt said. That’s where understanding the data engineering lifecycle comes in clutch. Holistic thinking, on the other hand, means getting past the technology and considering the big picture. “If you can become a better communicator as a stakeholder or as an engineer, you’ll be way more successful in delivering results.” “We need communication to make sure data engineers understand what the data is before they turn it into a useful product, and to make sure those products are the right things,” Matt explained. ↔️ Engineers need to clearly express what’s happening to data, and stakeholders need to clearly express what raw data is or what finished data should look like. Matt started out by tackling the two biggest challenges currently facing data engineers: Communication and holistic thinking.įor starters, communication needs to flow both ways. Plus, as a teaser (if you haven’t read this 💎 already), he discussed concepts from the Fundamentals of Data Engineering to introduce these stages to data practitioners of all flavors, to help them better collaborate with data engineers to deliver outstanding data products. In his talk, Matt split the data engineering lifecycle problems into 🔑 stages (generation, storage, ingestion, transformation, and serving). To help folks better understand the data engineering lifecycle, Matt Housley – co-founder of Ternary Data and co-author of the book Fundamentals of Data Engineering – broke it down during his Summer Community Days session titled, How Understanding the Data Engineering Lifecycle Helps Us All Work Better with Data Engineers. The main objective is to assist data engineers in creating reliable, high-quality data sets that are suitable for their intended use and can aid in business decision-making. This lifecycle incorporates structured and interconnected stages aimed at consistently delivering high-quality data engineering projects.

The data engineering lifecycle is a method for overseeing data engineering processes including data acquisition, integration, storage, processing, and analysis.

And because engineering makes the whole data machine run, understanding the lifecycle data moves through makes you a better data collaborator – regardless of your role. Sure, technology changes, but the goal of data engineers remains the same: To turn raw, messy, complex data into high-quality information other teams can use. While they can be both of those 👆 things, that doesn’t mean analysts and business users should just ignore everything data engineering-related. From there, they gained a reputation for being super complex and technical. Somewhere along the line, folks began defining data engineers as people who worked in specific technologies like Hadoop or Spark.

Data engineer etl processes