Introducing Databricks LakeFlow: A New Solution for Data Engineering

Databricks has launched Databricks LakeFlow, a new tool that makes data engineering simpler and more efficient. Let’s explore what LakeFlow offers and how it can help data teams.

What is Databricks LakeFlow?

LakeFlow is designed to handle every part of data engineering, from bringing in data to transforming and Orchestrating it. Data teams can now easily bring in data from sources like MySQL, Postgres, Oracle, Salesforce, Dynamics, Sharepoint, and Google Analytics. Plus, with Real-Time Mode for Apache Spark™, it enables super-fast data processing.

Lake Flow

Key Features of LakeFlow

  1. LakeFlow Connect:

LakeFlow Connect simplifies integrating data from various sources. With native, scalable connectors and integration with the Unity Catalog, it ensures strong data governance. Regardless of the data’s size, format, or location, LakeFlow Connect makes it easily accessible for analysis.


For example, Here a retail company uses LakeFlow Connect to pull in sales data from MySQL and Salesforce in real time. This quick integration lets them analyze data fast, helping them respond effectively to sales trends.

So, we set up an ingestion pipeline that pulls in Salesforce data and external orders, including five key columns.

2. LakeFlow Pipelines:

Built on Databricks Delta Live Tables, this feature automates real-time data pipelines. we can transform data and perform ETL using SQL or Python, and it supports low-latency streaming without requiring code changes. By combining batch and stream processing, LakeFlow Pipelines simplifies the creation and management of complex data transformations.

With LakeFlow, we can connect various data sources. Here, we’re connecting another pipeline to SQL Server, bringing in customer data. We’re linking both the orders and customer tables and performing transformations on this data.

After transforming the data, we join the orders and customer columns. Then, we generate an SQL query that combines the orders and customer tables.

Here is the result of combining both ingested pipelines.

3. LakeFlow Jobs:

This feature automates managing, monitoring, and delivering data workflows. It improves control flow capabilities and provides full visibility to spot and resolve data issues. LakeFlow Jobs streamlines deploying, orchestrating, and monitoring data pipelines, making it easier for data teams to achieve their data delivery goals.

Combining all the pipelines, we generate a dashboard that provides insights into revenue and products.

Addressing Data Engineering Challenges

Data engineering is essential for making data and AI accessible in businesses, but it can be complicated. Data teams often struggle with:

  • Integrating data from different systems
  • Maintaining complex data preparation processes
  • Dealing with disruptions from data failures and latency spikes
  • Using multiple tools for deploying and monitoring data quality

These challenges can lead to poor data quality, reliability issues, high costs, and a backlog of work. LakeFlow addresses these problems by providing a unified experience on the Databricks Data Intelligence Platform. With deep integrations with Unity Catalog and serverless computing, LakeFlow ensures efficient and scalable data engineering.

Availability and Future Prospects

LakeFlow is about to change data engineering with its new unified approach. They are starting with LakeFlow Connect, which will soon be available for preview. If you’re interested, you can join the waitlist to try out this innovative solution.

Join the Waitlist

To join the waitlist and be among the first to try Databricks LakeFlow,

click here.

The demo use case will be shared on the upcoming blog once the preview is available.
Stay tuned for the next blog.

For More Details, Diggibyte Technologies Pvt Ltd has all the experts you need. Contact us Today to embed intelligence into your organization.

Author: Nihalataskeen Inayathulla

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