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

Impact:  High Category:  Scalability

Databricks pools pre-provision VMs, reducing risks of provisioning errors during cluster start or scale, enhancing reliability.

Reduces provisioning errors

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ARG Query:

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Use SSD backed VMs for Worker VM Type and Driver type

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

Faster, reliable VM performance

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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.

Cost and performance optimization

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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.

Improves concurrency and efficiency

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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.

Optimized resource use and minimal latency

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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.

Saves cluster resources, avoids idle use

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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.

Improved troubleshooting and audit

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

Enhances data reliability and processing

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Apache Spark in Databricks Lakehouse ensures resilient distributed data processing by automatically rescheduling failed tasks, aiding in overcoming external issues like network problems or revoked VMs.

Boosts speed and reliability for Spark tasks

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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.

Enhanced data resilience and integrity

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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.

Enhanced reliability and autoscaling

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

Enhances scalability and reliability

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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.

Enhances data quality and trust

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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.

Enhanced data integrity and quality

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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.

Prevents invalid data and job failures

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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.

Ensures data quality and integrity

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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.

Ensures data recovery and migration

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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.

Fault-tolerance and auto-restart for queries

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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.

Easy rollback and fix for ETL jobs

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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.

Saves time and money with smart recovery

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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.

Ensures service continuity during disasters

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

Efficient, reliable automation

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

Enhanced reliability and automation

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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.

Enhanced limits management, team separation

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

Enhanced security and resource isolation

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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.

Enhanced security, data protection

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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.

Ensures high reliability for production

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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.

Improves resilience and data sovereignty

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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.

Ensures service availability

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