Data Pipelines vs a Data Lake

Data Pipelines vs a Data Lake
March 13, 2024

One of the common questions we hear from non-technical business leaders is this:

We have some of our data in a data lake, so what’s the difference between a data lake and data pipelines?

While the answer is straightforward to engineers, there’s a learning curve for business executives in big data architectures and AI.

The Data Lake

A data lake, just like the name describes, is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is without having to first structure it—“chucking” vast amounts of data in its native format into the cloud-based data lake until it’s needed. This can be any type of binary data text, images, or complex analytical data.

From your data lake, your analysts can run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.

To extract intelligence from the data lake, someone must decide on a use case and create the processing, model, or visualization for the intelligence.

The primary use of the data lake is for storage—giving data scientists and analysts a single repository of your data for completing project work. From it, they can run big data analytics, build machine learning models, and query complex datasets to uncover insights that were not visible before.

The Data Pipeline

A data pipeline, on the other hand, automates the flow of data and ensures its availability for analysis and decision-making—whether handled by AI output or human analysis. While a water pipeline uses pressure to move water from point A to point B, software performing actions (data processing steps) move and transform data from one system to another.

From a data pipeline, a person, algorithm, or model can use the continuous flow of data for analysis, reporting, and decision-making in a more efficient and automated manner.

Data pipelines often include steps for cleansing, aggregating, transforming, and combining data from disparate sources to make it more valuable and accessible for end-users or downstream applications. Unlike a data lake that can be loaded “off the shelf,” data pipelines require development and real-time processing of integrating various data sources and ensuring that data is consistently and efficiently processed and moved to where it’s needed.

Use in a Company’s Data Architecture

Most large enterprises have a data lake already in place. Popular solutions include:

  • AWS S3 buckets
  • Microsoft ADLS
  • Google Cloud Storage
  • Snowflake
  • Oracle Cloud Storage
  • Cloudera Data Platform
  • Databricks Delta Lake
  • IBM Cloud Storage

More mid-market companies are using data lakes if they have specific needs for big data analysis. However, they require engineers to set up, use, and maintain.

Many large enterprises have built data pipelines to deliver automated intelligence for valuable use cases. And most are exploring additional data pipeline use cases.

Data pipeline solutions can have a steep learning curve, even for an experienced engineer. Many have specific requirements that need to be understood before being deployed. Popular solutions include:

  • AWS Glue
  • AWS Data Pipeline
  • Google Cloud Dataflow
  • Azure Data Factory
  • Databricks (built on Apache Spark)
  • Snowflake
  • Apache Airflow
  • Fivetran

Because of the complexities, few mid-market companies (outside of software and technology companies) seem to be actively building modern data pipelines to automate the delivery of intelligence.

It can take months of work for even an experienced data scientist to build a single data pipeline. Common frustrations we’ve heard over the years include things like:

Glue is a pain when connecting with sources outside of AWS. It’s very rigid and doesn’t give us much control.”
“The learning curve on Airflow is steep, and it requires far more maintenance than most other platforms. It’s also overcomplicated.”
“Integration with other systems can be restrictive with Databricks. We can’t get the control we need and we’re going to be locked in.”
“Fivetran doesn’t play well with legacy systems. If you need custom connections, good luck.”

The Rehinged AI platform delivers the combination of the two as a SaaS offering, with customization handled by our engineering team. This eliminates the complexity for our clients and partners—enabling them to unlock the value of their data from the Rehinged AI platform.

Connect with us to learn more.

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