Lakeflow Join: Environment friendly and Straightforward Information Ingestion utilizing the SQL Server connector

Complexities of Extracting SQL Server Information 

Whereas digital native firms acknowledge AI’s important function in driving innovation, many nonetheless face challenges in making their knowledge available for downstream makes use of, corresponding to machine studying growth and superior analytics. For these organizations, supporting enterprise groups that depend on SQL Server means having knowledge engineering assets and sustaining customized connectors, making ready knowledge for analytics, and making certain it’s obtainable to knowledge groups for mannequin growth. Usually, this knowledge must be enriched with further sources and reworked earlier than it may inform data-driven choices.

Sustaining these processes shortly turns into complicated and brittle, slowing down innovation. That’s why Databricks developed Lakeflow Join, which incorporates built-in knowledge connectors for common databases, enterprise functions, and file sources. These connectors present environment friendly end-to-end, incremental ingestion, are versatile and simple to arrange, and are totally built-in with the Databricks Information Intelligence Platform for unified governance, observability, and orchestration. The brand new Lakeflow SQL Server connector is the primary database connector with strong integration for each on-premises and cloud databases to assist derive knowledge insights from inside Databricks.

On this weblog, we’ll assessment the important thing concerns for when to make use of Lakeflow Join for SQL Server and clarify the right way to configure the connector to duplicate knowledge from an Azure SQL Server occasion. Then, we’ll assessment a selected use case, greatest practices, and the right way to get began. 

Key Architectural Issues

Under are the important thing concerns to assist resolve when to make use of the SQL Server connector.

Area & Function Compatibility 

Lakeflow Join helps a wide selection of SQL Server database variations, together with Microsoft Azure SQL Database, Amazon RDS for SQL Server, Microsoft SQL Server operating on Azure VMs and Amazon EC2, and on-premises SQL Server accessed by Azure ExpressRoute or AWS Direct Join.

Since Lakeflow Join runs on Serverless pipelines underneath the hood, built-in options corresponding to pipeline observability, occasion log alerting, and lakehouse monitoring will be leveraged. If Serverless just isn’t supported in your area, work together with your Databricks Account group to file a request to assist prioritize growth or deployment in that area. 

Lakeflow Join is constructed on the Information Intelligence Platform, which gives seamless integration with Unity Catalog (UC) to reuse established permissions and entry controls throughout new SQL Server sources for unified governance. In case your Databricks tables and views are on Hive, we suggest upgrading them to UC to learn from these options (AWS | Azure | GCP)!

Change Information Necessities 

Lakeflow Join will be built-in with an SQL Server with Microsoft change monitoring (CT) or Microsoft Change Information Seize (CDC) enabled to assist environment friendly, incremental ingestion. 

CDC gives historic change details about insert, replace, and delete operations, and when the precise knowledge has modified. Change monitoring identifies which rows have been modified in a desk with out capturing the precise knowledge adjustments themselves. Be taught extra about CDC and the advantages of utilizing CDC with SQL Server. 

Databricks recommends utilizing change monitoring for any desk with a major key to attenuate the load on the supply database. For supply tables and not using a major key, use CDC. Be taught extra about when to make use of it right here.

The SQL Server connector captures an preliminary load of historic knowledge on the primary run of your ingestion pipeline. Then, the connector tracks and ingests solely the adjustments made to the info for the reason that final run, leveraging SQL Server’s CT/CDC options to streamline operations and effectivity.

Governance & Personal Networking Safety 

When a connection is established with a SQL Server utilizing Lakeflow Join: 

• Site visitors between the consumer interface and the management aircraft is encrypted in transit utilizing TLS 1.2 or later.
• The staging quantity, the place uncooked recordsdata are saved throughout ingestion, is encrypted by the underlying cloud storage supplier.
• Information at relaxation is protected following greatest practices and compliance requirements. 
• When configured with non-public endpoints, all knowledge visitors stays throughout the cloud supplier’s non-public community, avoiding the general public web. 

As soon as the info is ingested into Databricks, it’s encrypted like different datasets inside UC. The ingestion gateway that extracts snapshots, change logs, and metadata from the supply database lands in a UC Quantity, a storage abstraction greatest for registering non-tabular datasets corresponding to JSON recordsdata. This UC Quantity resides throughout the buyer’s cloud storage account inside their Digital Networks or Digital Personal Clouds. 

Moreover, UC enforces fine-grained entry controls and maintains audit trails to control entry to this newly ingested knowledge. UC Service credentials and Storage Credentials are saved as securable objects inside UC, making certain safe and centralized authentication administration. These credentials are by no means uncovered in logs or hardcoded into SQL ingestion pipelines, offering strong safety and entry management.

In case your group meets the above standards, think about Lakeflow Join for SQL Server to assist simplify knowledge ingestion into Databricks.

Breakdown of Technical Answer

Subsequent, assessment the steps for configuring Lakeflow Join for SQL Server and replicating knowledge from an Azure SQL Server occasion.

Configure Unity Catalog Permissions

Inside Databricks, guarantee serverless compute is enabled for notebooks, workflows, and pipelines (AWS | Azure | GCP). Then, validate that the person or service principal creating the ingestion pipeline has the next UC permissions: 

Arrange Azure SQL Server

To make use of the SQL Server connector, affirm that the next necessities are met:

• Verify SQL Model
  • SQL Server 2012 or a later model should be enabled to make use of change monitoring. Nonetheless, 2016+ is beneficial*. Overview SQL Model necessities right here.
• Configure the Database service account devoted to the Databricks ingestion. 
  • Validate privilege necessities primarily based on cloud (AWS | Azure | GCP)
• Allow change monitoring or built-in CDC 
  • You will need to have SQL Server 2012 or a later model to make use of CDC. Variations sooner than SQL Server 2016 moreover require the Enterprise version.

* Necessities as of Might 2025. Topic to alter.

Instance: Ingesting from Azure SQL Server to Databricks

Subsequent, we’ll ingest a desk from an Azure SQL Server database to Databricks utilizing Lakeflow Join. On this instance, CDC and CT present an outline of all obtainable choices. For the reason that desk on this instance has a major key, CT may have been the first selection. Nonetheless, since there is just one small desk on this instance, there isn’t a concern about load overhead, so CDC was additionally included. It is strongly recommended to assessment when to make use of CDC, CT, or each to find out which is greatest to your knowledge and refresh necessities. 

1. [Azure SQL Server] Confirm and Configure Azure SQL Server for CDC and CT

Begin by accessing the Azure portal and signing in utilizing your Azure account credentials. On the left-hand aspect, click on All companies and seek for SQL Servers. Discover and click on your server, and click on the ‘Question Editor’; on this instance, sqlserver01 was chosen. 

The screenshot under reveals that the SQL Server database has one desk referred to as ‘drivers’.

Earlier than replicating the info to Databricks, both change knowledge seize, change monitoring, or each should be enabled. 

For this instance,  the next script is run on the database to allow CT:

This command permits change monitoring for the database with the next parameters:

• CHANGE_RETENTION = 3 DAYS: This worth tracks adjustments for 3 days (72 hours). A full refresh shall be required in case your gateway is offline longer than the set time. It is strongly recommended that this worth be elevated if extra prolonged outages are anticipated.
• AUTO_CLEANUP = ON: That is the default setting. To keep up efficiency, it mechanically removes change monitoring knowledge older than the retention interval.

Then, the next script is run on the database to allow CDC:

When each scripts end operating, assessment the tables part underneath the SQL Server occasion in Azure and be certain that all CDC and CT tables are created. 

2. [Databricks] Configure the SQL Server connector in Lakeflow Join

On this subsequent step, the Databricks UI shall be proven to configure the SQL Server connector. Alternatively, Databricks Asset Bundles (DABs), a programmatic approach to handle the Lakeflow Join pipelines as code, may also be leveraged. An instance of the total DABs script is within the appendix under.

As soon as all of the permissions are set, as specified by the Permission Conditions part, you might be able to ingest knowledge. Click on the + New button on the prime left, then choose Add or Add knowledge. 

Then choose the SQL Server choice.

The SQL Server connector is configured in a number of steps. 

1. Arrange the ingestion gateway (AWS | Azure | GCP). On this step, present a reputation for the ingestion gateway pipeline and a catalog and schema for the UC Quantity location to extract snapshots and frequently change knowledge from the supply database.

2. Configure the ingestion pipeline. This replicates the CDC/CT knowledge supply and the schema evolution occasions. A SQL Server connection is required, which is created by the UI following these steps or with the next SQL code under:

For this instance, identify the SQL server connection insurgent as proven. 

3. Choosing the SQL Server tables for replication. Choose the entire schema to be ingested into Databricks as an alternative of selecting particular person tables to ingest.

The entire schema will be ingested into Databricks throughout preliminary exploration or migrations. If the schema is giant or exceeds the allowed variety of tables per pipeline (see connector limits), Databricks recommends splitting the ingestion throughout a number of pipelines to keep up optimum efficiency. To be used case-specific workflows corresponding to a single ML mannequin, dashboard, or report, it’s usually extra environment friendly to ingest particular person tables tailor-made to that particular want, moderately than the entire schema.

4. Configure the vacation spot the place the SQL Server tables shall be replicated inside UC. Choose the primary catalog and sqlserver01 schema to land the info in UC.

5. Configure schedules and notifications (AWS | Azure | GCP). This closing step will assist decide how typically to run the pipeline and the place success or failure messages ought to be despatched. Set the pipeline to run each 6 hours and notify the person solely of pipeline failures. This interval will be configured to satisfy the wants of your workload.

The ingestion pipeline will be triggered on a customized schedule. Lakeflow Join will mechanically create a devoted job for every scheduled pipeline set off. The ingestion pipeline is a activity throughout the job. Optionally, extra duties will be added earlier than or after the ingestion activity for any downstream processing.

After this step, the ingestion pipeline is saved and triggered, beginning a full knowledge load from the SQL Server into Databricks.

3. [Databricks] Validate Profitable Runs of the Gateway and Ingestion Pipelines

Navigate to the Pipeline menu to examine if the gateway ingestion pipeline is operating. As soon as full, seek for ‘update_progress’ throughout the pipeline occasion log interface on the backside pane to make sure the gateway efficiently ingests the supply knowledge.

To examine the sync standing, navigate to the pipeline menu. The screenshot under reveals that the ingestion pipeline has carried out three insert and replace (UPSERT) operations.

Navigate to the goal catalog, primary, and schema, sqlserver01, to view the replicated desk, as proven under.

4. [Databricks] Take a look at CDC and Schema Evolution

Subsequent, confirm a CDC occasion by performing insert, replace, and delete operations within the supply desk. The screenshot of the Azure SQL Server under depicts the three occasions.

As soon as the pipeline is triggered and is accomplished, question the delta desk underneath the goal schema and confirm the adjustments.

Equally, let’s carry out a schema evolution occasion and add a column to the SQL Server supply desk, as proven under

After altering the sources, set off the ingestion pipeline by clicking the beginning button throughout the Databricks DLT UI. As soon as the pipeline has been accomplished, confirm the adjustments by looking the goal desk, as proven under. The brand new column electronic mail shall be appended to the tip of the drivers desk.

5. [Databricks] Steady Pipeline Monitoring 

Monitoring their well being and habits is essential as soon as the ingestion and gateway pipelines are efficiently operating. The pipeline UI gives knowledge high quality checks, pipeline progress, and knowledge lineage data. To view the occasion log entries within the pipeline UI, find the underside pane underneath the pipeline DAG, as proven under. 

The occasion log entry above reveals that the ‘drives_snapshot_flow’ was ingested from the SQL Server and accomplished. The maturity stage of STABLE signifies that the schema is secure and has not modified. Extra data on the occasion log schema will be discovered right here.

Actual-World Instance

A big-scale medical diagnostic lab utilizing Databricks confronted challenges effectively ingesting SQL Server knowledge into its lakehouse. Earlier than implementing Lakeflow Join, the lab used Databricks Spark notebooks to drag two tables from Azure SQL Server into Databricks. Their utility would then work together with the Databricks API to handle compute and job execution. 

The medical diagnostic lab carried out Lakeflow Join for SQL Server, recognizing that this course of may very well be simplified. As soon as enabled, the implementation was accomplished in simply in the future, permitting the medical diagnostic lab to leverage Databricks’ built-in instruments for observability with every day incremental ingestion refreshes. 

Operational Issues

As soon as the SQL Server connector has efficiently established a connection to your Azure SQL Database, the following step is to effectively schedule your knowledge pipelines to optimize efficiency and useful resource utilization. As well as, it is important to observe greatest practices for programmatic pipeline configuration to make sure scalability and consistency throughout environments.

Pipeline Orchestration 

There isn’t a restrict on how typically the ingestion pipeline will be scheduled to run. Nonetheless, to attenuate prices and guarantee consistency in pipeline executions with out overlap, Databricks recommends not less than a 5-minute interval between ingestion executions. This permits new knowledge to be launched on the supply whereas accounting for computational assets and startup time. 

The ingestion pipeline will be configured as a activity inside a job. When downstream workloads depend on recent knowledge arrival, activity dependencies will be set to make sure the ingestion pipeline run completes earlier than executing downstream duties.

Moreover, suppose the pipeline remains to be operating when the following refresh is scheduled. In that case, the ingestion pipeline will behave equally to a job and skip the replace till the following scheduled one, assuming the at the moment operating replace completes on time.

Observability & Value Monitoring 

Lakeflow Join operates on a compute-based pricing mannequin, making certain effectivity and scalability for varied knowledge integration wants. The ingestion pipeline operates on serverless compute, which permits for flexibility in scaling primarily based on demand and simplifies administration by eliminating the necessity for customers to configure and handle the underlying infrastructure.

Nonetheless, it is essential to notice that whereas the ingestion pipeline can run on serverless compute, the ingestion gateway for database connectors at the moment operates on basic compute to simplify connections to the database supply. In consequence, customers would possibly see a mixture of basic and serverless DLT DBU fees mirrored of their billing.

The best approach to monitor and monitor Lakeflow Join utilization is thru system tables. Under is an instance question to view a specific Lakeflow Join pipeline’s utilization:

The official pricing for Lakeflow Join documentation (AWS | Azure | GCP) gives detailed charge data. Further prices, corresponding to serverless egress charges (pricing), might apply. Egress prices from the Cloud supplier for traditional compute will be discovered right here (AWS | Azure | GCP).

Greatest Practices and Key Takeaways

As of Might 2025, under are among the greatest practices and concerns to observe when implementing this SQL Server connector:

1. Configure every Ingestion Gateway to authenticate with a person or entity with entry solely to the replicated supply database.
2. Make sure the person ​​is given the required permissions to create connections in UC and ingest the info.
3. Make the most of DABs to reliably configure Lakeflow Join ingestion pipelines, making certain repeatability and consistency in infrastructure administration.
4. For supply tables with major keys, allow Change Monitoring to attain decrease overhead and improved efficiency.
5. For supply tables and not using a major key, allow CDC on account of its potential to seize adjustments on the column stage, even with out distinctive row identifiers.

Lakeflow Join for SQL Server gives a totally managed, built-in integration for each on-premises and cloud databases for environment friendly, incremental ingestion into Databricks.

Subsequent Steps & Further Assets

Attempt the SQL Server connector at the moment to assist resolve your knowledge ingestion challenges. Comply with the steps outlined on this weblog or assessment the documentation. Be taught extra about Lakeflow Join on the product web page, view a product tour or view a demo of the Salesforce connector to assist predict buyer churn.

Databricks Supply Options Architects (DSAs) speed up Information and AI initiatives throughout organizations. They supply architectural management, optimize platforms for price and efficiency, improve developer expertise, and drive profitable undertaking execution. DSAs bridge the hole between preliminary deployment and production-grade options, working carefully with varied groups, together with knowledge engineering, technical leads, executives, and different stakeholders to make sure tailor-made options and sooner time to worth. To learn from a customized execution plan, strategic steerage, and assist all through your knowledge and AI journey from a DSA, please contact your Databricks Account Staff.

Appendix

On this non-obligatory step, to handle the Lakeflow Join pipelines as code utilizing DABs, you merely want so as to add two recordsdata to your present bundle:

• A workflow file that controls the frequency of knowledge ingestion (assets/sqlserver.yml).
• A pipeline definition file (assets/sqlserver_pipeline.yml).

assets/sqlserver.yml:

assets/sqlserver_job.yml: