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The 6 pillars of data engineering needed for a 360-degree view of your business

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Countless organizations have completed a digital transformation within the past handful of years, but this has led to many of them pulling in more raw data than they know what to do with.

This is a good problem to have, but a problem nonetheless.

Business leaders know that data is one of the most valuable resources an organization can have. But in order to leverage that data, you need the proper architecture, tools, and processes to make it usable for analytics purposes and to identify actionable insights that drive business value.

That’s where the six pillars of data engineering come into play.

This article will focus on the six pillars of data engineering that are needed to leverage your organization’s data effectively, as well as some of the tools that can help you in your data endeavors.

With these pillars firmly in place, you’ll be able to transform that data into a crystal clear picture of what can be improved within your organization and what opportunities can be taken advantage of to gain a competitive advantage.

1. Data Ingestion

It’s likely your business has a multitude of data streams that originate from different sources — from databases, applications, and IoT devices, for example. Before you can analyze that data and obtain actionable insights for your business, you need a way of gathering all that data in one place.

It needs to be ingested.

Data ingestion is the process of transporting data from those sources to a unified environment, such as a cloud data lake.

But more needs to be done . . .

Tools: Apache Kafka/PyKafka, AWS Kinesis/Kinesis Data Streams/Kinesis Firehose, Spark Streaming, RabbitMQ, Google PubSub, Azure Data Bricks

2. Data Integration

Once the data is transported to a unified environment, data integration takes it a step further.

Data integration ensures that all data that was ingested is compatible with each other in order to be used for downstream applications or analytics.

There are different approaches when it comes to data integration, and they both have their strengths and weaknesses.

One approach, ETL (extract, transform, load), is the more traditional one and is ideal when security, quality, and compliance are concerns.

ELT (extract, load, transform) has become an increasingly popular method of data integration with the adoption of cloud and agile methodologies. This approach is best suited for large volumes of unstructured data.

But there’s also another integration method, which is a hybrid of the two: ETLT (extract, transform, load, transform). This method aims to get the best of both worlds of ETL and ELT.

Tools: Spark – PySpark, Flink – PyFlink, AWS Glue, Apache Beam, GCP Data Flow, Apache Hive, Azure Data Factory

3. Data Modeling

The third pillar of data engineering is data modeling. It’s essentially a way to visualize the data that was pulled so that it’s easier to see the relationships between data sources and how data flows between those sources.

Data modeling is integral to fully understand your data and to support your business’s analytical needs.

Tools: Amazon Athena, Amazon Redshift, Google Cloud BigQuery, Azure Synapse Analytics, Azure Cosmos DB, MongoDB

4. Data Observability

Data observability provides users with visibility into the health and state of data. It can detect situations your team wasn’t aware of or even thought to plan for. With data observability, data teams can be alerted in real time in order to identify, troubleshoot, and resolve data issues, such as schema drift and duplicate values, before they have time to impact the business.

As different departments within an organization often need to make decisions based on the same data, it’s imperative that the data is accurate and reliable in order to inform those decisions and prevent costly errors and downtime.

Tools: AWS Wrangler, AWS Glue Databrew, Apache Airflow, Apache Sqoop

5. DataOps

The rise of Big Data has necessitated yet another Ops in addition to DevOps: DataOps.

Emphasizing collaboration, DataOps is an agile-based method that bridges the gaps between data engineers, data scientists, and other stakeholders within the organization who rely on data. It brings together the tools, people, processes, and the data itself.

What DevOps did for software development is what DataOps is doing for organizations that want to be truly data driven.

The end goal of DataOps is to streamline the creation and maintenance of data pipelines, ensure the quality of that data for the applications that depend on it, and to create the most business value from that data.

Tools: AWS Wrangler, AWS Glue Databrew, Apache Airflow, Apache Sqoop

6. Data Delivery

We’ve reached the final pillar of data engineering — and the final output of a data pipeline. This is the “connect between data pipeline and data analytics.  Data delivery is where data pipelines meet data analytics. This pillar entails making the data available in specific ways so that the data can be consumed by downstream systems. Once the data is made available, it can be consumed and used for the three types of analytics that drive decision-making: descriptive (what’s happened), predictive (what could happen), and prescriptive analytics (what should happen).

Tools: Amazon Quicksight, Tableau, Microsoft Power BI, Graffana, Plotly, Matplotlib

Conclusion

With organizations across every industry becoming data driven and it becoming increasingly difficult to build and maintain data pipelines, it’s important to implement automated processes and tools that can make that data usable and more valuable.

What Relevantz Can Do for You

Relevantz helps enterprises get more from their data. With our data platform services, we build end-to-end data engineering pipelines — covering all six pillars of data engineering — as well as perform modernization, migration, and maintenance of those pipelines to keep the data flowing like it should.