workspaces

APRL GUID:  0e835cc2-2551-a247-b1f1-3c5f25c9cb70

Description:

Databricks recommends migrating workloads to the latest or LTS version of its runtime for enhanced stability and support. If on Runtime 11.3 LTS or above, move directly to the latest 12.x version. If below, first migrate to 11.3 LTS, then to the latest 12.x version as per the migration guide.

Potential Benefits:

Enhanced stability and support

APRL GUID:  5877a510-8444-7a4c-8412-a8dab8662f7e

Description:

Upgrade HDDs in premium VMs to SSDs for better speed and reliability. Premium SSDs boost IO-heavy apps; Standard SSDs balance cost and performance. Ideal for critical workloads, upgrading improves connectivity with brief reboot. Consider for vital VMs

Potential Benefits:

Faster, reliable VM performance

APRL GUID:  5c72f0d6-55ec-d941-be84-36c194fa78c0

Description:

Autoscaling adjusts cluster sizes automatically based on workload demands, offering benefits for many use cases in terms of costs and performance. It includes guidance on when and how to best utilize Autoscaling. For streaming, Delta Live Tables with autoscaling is advised.

Potential Benefits:

Cost and performance optimization

APRL GUID:  362ad2b6-b92c-414f-980a-0cf69467ccce

Description:

The scaling parameter of a SQL warehouse defines the min and max number of clusters for distributing queries. By default, it's set to one. Increasing the cluster count can accommodate more concurrent users effectively.

Potential Benefits:

Improves concurrency and efficiency

APRL GUID:  cd77db98-9b13-6e4b-bd2b-74c2cb538628

Description:

Databricks enhanced autoscaling optimizes cluster utilization by automatically allocating cluster resources based on workload volume, with minimal impact on the data processing latency of your pipelines.

Potential Benefits:

Optimized resource use and minimal latency

APRL GUID:  3d3e53b5-ebd1-db42-b43b-d4fad74824ec

Description:

To conserve cluster resources, you can terminate a cluster to store its configuration for future reuse or autostart jobs. Clusters can auto-terminate after inactivity, but this only tracks Spark jobs, not local processes, which might still be running even after Spark jobs end.

Potential Benefits:

Saves cluster resources, avoids idle use

APRL GUID:  7fb90127-5364-bb4d-86fa-30778ed713fb

Description:

When creating a Databricks cluster, you can set a log delivery location for the Spark driver, worker nodes, and events. Logs are delivered every 5 mins and archived hourly. Upon cluster termination, all generated logs until that point are guaranteed to be delivered.

Potential Benefits:

Improved troubleshooting and audit

APRL GUID:  da4ea916-4df3-8c4d-8060-17b49da45977

Description:

Delta Lake is an open source storage format enhancing data lakes' reliability with ACID transactions, schema enforcement, and scalable metadata handling.

Potential Benefits:

Enhances data reliability and processing

APRL GUID:  7e52d64d-8cc0-8548-a593-eb49ab45630d

Description:

Invalid or nonconforming data can crash workloads dependent on specific data formats. Best practices recommend filtering such data at ingestion to improve end-to-end resilience, ensuring no data is lost or missed.

Potential Benefits:

Enhanced data resilience and integrity

APRL GUID:  84e44da6-8cd7-b349-b02c-c8bf72cf587c

Description:

Use Databricks and MLflow for deploying models as Spark UDFs for job scheduling, retries, autoscaling. Model serving offers scalable infrastructure, processes models using MLflow, and serves them via REST API using serverless compute managed in Databricks cloud.

Potential Benefits:

Enhanced reliability and autoscaling

APRL GUID:  4cbb7744-ff3d-0447-badb-baf068c95696

Description:

Use Databricks and MLflow for deploying models as Apache Spark UDFs, benefiting from job scheduling, retries, autoscaling, etc.

Potential Benefits:

Enhances scalability and reliability

APRL GUID:  1b0d0893-bf0e-8f4c-9dc6-f18f145c1ecf

Description:

Curate data by creating a layered architecture to increase data quality across layers. Start with a raw layer for ingested source data, continue with a curated layer for cleansed and refined data, and finish with a final layer catered to business needs, focusing on security and performance.

Potential Benefits:

Enhances data quality and trust

APRL GUID:  e93fe702-e385-d741-ba37-1f1656482ecd

Description:

Copying data leads to redundancy, lost integrity, lineage, and access issues, affecting lakehouse data quality. Temporary copies are useful for agility and innovation but can become problematic operational data silos, questioning data's master status and currency.

Potential Benefits:

Enhanced data integrity and quality

APRL GUID:  b7e1d13f-54c9-1648-8a52-34c0abe8ce16

Description:

Uncontrolled schema changes can lead to invalid data and failing jobs. Databricks validates and enforces schema through Delta Lake, which prevents bad records during ingestion, and Auto Loader, which detects new columns and supports schema evolution to maintain data integrity.

Potential Benefits:

Prevents invalid data and job failures

APRL GUID:  a42297c4-7e4f-8b41-8d4b-114033263f0e

Description:

Delta tables verify data quality automatically with SQL constraints, triggering an error for violations. Delta Live Tables enhance this by defining expectations for data quality, utilizing Python or SQL, to manage actions for record failures, ensuring data integrity and compliance.

Potential Benefits:

Ensures data quality and integrity

APRL GUID:  932d45d6-b46d-e341-abfb-d97bce832f1f

Description:

To recover from a failure, regular backups are needed. The Databricks Labs project migrate lets admins create backups by exporting workspace assets using the Databricks CLI/API. These backups help in restoring or migrating workspaces.

Potential Benefits:

Ensures data recovery and migration

APRL GUID:  12e9d852-5cdc-2743-bffe-ee21f2ef7781

Description:

Structured Streaming ensures fault-tolerance and data consistency in streaming queries. With Azure Databricks workflows, you can set up your queries to automatically restart after failure, picking up precisely where they left off.

Potential Benefits:

Fault-tolerance and auto-restart for queries

APRL GUID:  a18d60f8-c98c-ba4e-ad6e-2fac72879df1

Description:

Despite thorough testing, a production job can fail or yield unexpected data. Sometimes, repairs are done by adding jobs post-issue identification and pipeline correction.

Potential Benefits:

Easy rollback and fix for ETL jobs

APRL GUID:  c0e22580-3819-444d-8546-a80e4ed85c83

Description:

Databricks Workflows enable efficient error recovery in multi-task jobs by offering a matrix view for issue examination. Fixes can be applied to initiate repair runs targeting only failed and dependent tasks, preserving successful outcomes and thereby saving time and money.

Potential Benefits:

Saves time and money with smart recovery

APRL GUID:  4fdb7112-4531-6f48-b60e-c917a6068d9b

Description:

Implementing a disaster recovery pattern is vital for Azure Databricks, ensuring data teams' access even during rare regional outages.

It is important to note that the Azure Databricks service is not entirely zone redudant and does support zonal failover.

Potential Benefits:

Ensures service continuity during disasters

APRL GUID:  42aedaa8-6151-424d-b782-b8666c779969

Description:

The Databricks Terraform provider manages Azure Databricks workspaces and cloud infrastructure flexibly and powerfully.

Potential Benefits:

Efficient, reliable automation

APRL GUID:  20193ff9-dbcd-a74e-b197-71d7d9d3c1e6

Description:

The Databricks Terraform provider is a flexible, powerful tool for managing Azure Databricks workspaces and cloud infrastructure.

Potential Benefits:

Enhanced reliability and automation

APRL GUID:  397cdebb-9d6e-ab4f-83a1-8c481de0a3a7

Description:

Customers often naturally divide workspaces by teams or departments. However, it's crucial to also consider Azure Subscription and Azure Databricks (ADB) Workspace limits when partitioning.

Potential Benefits:

Enhanced limits management, team separation

APRL GUID:  5e722c4f-415a-9b4c-bd4c-96b74dce29ad

Description:

Deploying only one Databricks Workspace per VNet aligns with Azure Databricks' isolation model.

Potential Benefits:

Enhanced security and resource isolation

APRL GUID:  14310ba6-77ad-3641-a2db-57a2218b9bc7

Description:

Driven by security and data availability concerns, each Azure Databricks Workspace comes with a default DBFS designed for system-level artifacts like libraries and Init scripts, not for production data.

Potential Benefits:

Enhanced security, data protection

APRL GUID:  b5af7e26-3939-1b48-8fba-f8d4a475c67a

Description:

Azure Spot VMs are not suitable for critical production workloads needing high availability and reliability. They are meant for fault-tolerant tasks and can be evicted with 30-seconds notice if Azure needs the capacity, with no SLA guarantees.

Potential Benefits:

Ensures high reliability for production

APRL GUID:  8aa63c34-dd9d-49bd-9582-21ec310dfbdd

Description:

Move workspaces to in-region control plane for increased regional isolation. Identify current control plane region using the workspace URL and nslookup. When region from CNAME differs from workspace region and an in-region control is available, consider migration using tools provided below.

Potential Benefits:

Improves resilience and data sovereignty

APRL GUID:  028593be-956e-4736-bccf-074cb10b92f4

Description:

Azure Databricks planning should include VM SKU swap strategies for capacity issues. VMs are regional, and allocation failures may occur, shown by a "CLOUD PROVIDER" error.

Potential Benefits:

Ensures service availability

APRL GUID:  e94da1f8-33e7-48a6-b301-72f19a53bc29

Description:

Where possible Leverage managed services (serverless compute) from the Databricks Data Intelligence Platform, such as:serverless SQL warehouses, model serving, serverless jobs, serverless compute for notebooks.

Potential Benefits:

Improve resiliency and scalability at no additional cost