What are the best practices for using Databricks in data engineering?

1. Workload Analysis: Assess the nature of your workloads, such as batch processing, real-time streaming, or machine learning, to determine the cluster’s requirements. 2. Cluster Sizing: Choose the appropriate number of worker nodes, instance types, and memory capacity based on workload demands. 3. Dynamic Clusters: Implement auto-scaling to automatically adjust cluster size to workload fluctuations, optimizing resource utilization and cost. 4. Node Initialization Scripts: Use initialization scripts to customize the environment for your workloads, ensuring efficient cluster setup. 5. Spot Instances: Consider using AWS Spot Instances for cost savings, especially for fault-tolerant and non-time-sensitive workloads.

Choosing the right cluster configuration can have a major impact on performance and costs. Good point about matching the cluster mode and node type to the specific data workload. Far too often I've seen teams either overpay for more power than they need, or become frustrated with slow jobs from an underpowered cluster. Recommending single node clusters for testing/dev is a great tip as well. That's an area I've overlooked in the past. My main suggestion would be to also highlight spot instances and auto-termination. Using spot instances can cut costs dramatically, and auto-termination ensures you don't waste money with idle clusters.

'Right' is the key word in 'right cluster configuration'. Whether to enable auto-scaling and how long to terminate server before inactivity depends on the requirements. Sometimes, tradeoff has to be made. For example, while auto-scaling enables cost saving, it could not achieve the same level of performance when auto-scaling is off.

Leveraging Delta Lake for data reliability is essential in data engineering. 1. ACID Compliance: Delta Lake provides ACID (Atomicity, Consistency, Isolation, Durability) compliance for data transactions, ensuring data integrity. 2. Versioning: It allows for versioning and history tracking, enabling easy data recovery and rollback. 3. Data Quality: Delta Lake helps maintain data quality with schema enforcement and data integrity checks. 4. Time Travel: The time travel feature allows you to access data snapshots at various points in time for auditing and debugging. 5. Reliable Streaming: Delta Lake supports reliable streaming to ensure fault tolerance and exactly-once processing.

Delta Lake helps avoid many of the common headaches we deal with, like data corruption and siloed data. The ability to enforce schemas and merge datasets is a total game changer! One tip I'd add is to leverage Delta Lake's time travel capabilities for data pipeline debugging. I've used the versioning to "rewind" corrupted data and identify the source of issues. It's saved me countless hours compared to rebuilding datasets from scratch. Also, for new users, I'd emphasise using the DELTA data source in Spark SQL. It makes querying Delta tables just like querying regular tables. Delta Lake is an essential part of modern data architecture in my experience. The key benefits - ACID compliance, schema enforcement, versioning, etc.

Following best coding practices is crucial for maintaining data engineering projects. 1. Consistency: Adhere to a consistent code style and naming conventions for clarity. 2. Modularity: Break code into reusable functions and libraries to minimize redundancy. 3. Documentation: Provide clear and extensive documentation for code, functions, and workflows. 4. Testing: Develop unit tests and integration tests to validate code functionality. 5. Code Reviews: Implement code reviews to ensure quality and adherence to best practices.

We often focus more on the algorithms and models and neglect the importance of clear, reusable code. Your tips on using meaningful names, adding documentation, and avoiding hard-coded values are spot on. Those simple habits make our code so much more maintainable over time. I couldn't agree more on leveraging notebooks and libraries as well. Notebooks are invaluable for organising workflow steps and enabling collaboration. And libraries are a lifesaver for sharing common code across teams. One coding best practice I'd add is to use object-oriented programming and classes wherever possible. For Python users, tools like PEP8 linting help enforce standards. Overall, high-quality code pays dividends across any data platform.

Readability is critical in any large scale platform or applications. Codes need to be maintained and supported. New features needed to be added. Most likely these activities will be done by different developers overtime. Readability in Databricks can mean at least the following: 1. name all technical artifacts such as notebooks, folders, scheme, table, consistently following standard 2. All names including variables, functions, classes in coding, should be self explanatory and unambiguous 3. Using markup cell to structure and document notebooks. 4. Complex coding block needs more detailed information or instructions

Monitoring and optimizing your data pipeline is critical for data engineering. 1. Continuous Monitoring: Implement monitoring tools to track pipeline performance and identify bottlenecks or failures. 2. Alerting: Set up alerts for unusual or critical events to respond promptly. 3. Performance Tuning: Optimize data processing, storage, and ETL processes for efficiency. 4. Resource Allocation: Allocate resources appropriately to match workload demands, using autoscaling when possible. 5. Cost Control: Keep an eye on costs and optimize resource usage to stay within budget.

Monitoring and optimization - the never-ending quest for every data engineer! As someone who has spent many late nights firefighting production pipelines, I cannot agree more on the criticality of actively monitoring jobs. One additional tip I'd recommend is to establish alerting based on key metrics like job duration, memory utilisation, and I/O rates. Simple alerts on thresholds can notify teams of performance drifts or bottlenecks as they emerge. This gives data engineers a chance to proactively tune jobs versus fighting fires. Tools like MLflow provide deeper insight into model performance over time. Leveraging MLflow MODEL_VERSION and STAGE_TRANSITION events enables monitoring of how new models impact job metrics when promoted.

One useful feature called ‘Volume’ in Databricks will allow for a finer grain access control of the unstructured files stored in the cloud storage. Needless to say, it also requires Unity Catalog to be enabled.

Securing and protecting data assets is paramount in data engineering. 1. Data Encryption: Implement encryption at rest and in transit to safeguard data. 2. Access Control: Enforce role-based access controls to limit data access to authorized users. 3. Audit Logging: Enable comprehensive audit logs to track data access and changes. 4. Data Backups: Regularly backup data to prevent loss from hardware failures or data corruption. 5. Data Privacy: Comply with data privacy regulations, such as GDPR, to protect sensitive information.

Use Unity Catalog to protect all your data assets (tables, views, ML Models, volumes) using one tool to apply data governance framework, this will minimise the effort required to protect your data assets and allow for more data discovery in your organisation. You can apply permissions on Unity catalog using simple SQL statement which makes it easy for data practitioners to get themselves up to speed with minimum effort

Protecting data assets and user privacy should be at the foundation of any data platform. Enabling Databricks' access controls, encryption, and secrets management capabilities is spot on. Those tools allow teams to implement security best practices in a reliable way. Role-based access and auditing are crucial as well to manage permissions and have visibility into data usage. One best practice I'd suggest is establishing automated checks for security configurations using tools like policy-as-code. This helps catch issues like ACL misconfigurations or encryption keys rotating. Regular security reviews and penetration testing are also important to validate controls. No technology can replace proactive security management.

Another way to upskill Databricks is to watch sessions from AI Summit. There are plenty of product updates, case study, tech deep dives for you to learn and stay abreast with latest technology

Run auto-optimisation and auto-tune features in Databricks, this will help optimise the data layout with minimum engineering overhead, use liquid clustering to avoid creating a wrong pertaining strategy or wrong indexes. Run Vacuum periodically, archive old/ infrequently accessed records.