Can you answer this question: Do you know how to make your clients happy?
The always-winning answer: Reduce costs and speed up frequent tasks. But there’s more.
In data engineering, using technologies like Databricks Delta Tables, Spark, and Azure Databricks properly can further optimize your processes.
Let me introduce myself and the story I want to tell you today. I am Mahmood (Just call me Moe!). A data engineer that enjoys finding new methods for optimizing data systems and enhancing ETL workflows.
One of my clients, a global producer of material handling machinery, had recently implemented Databricks on Azure, hoping to reduce costs and improve processing speed of their data solutions. However, they were not satisfied with their results and called Oliva Advisory to help them set up best practices.
This article covers the best practices I helped them apply, in another article, referenced here "Top 8 Beginner Mistakes with Databricks Delta Lake and How to Avoid Them", I discuss common beginner mistakes with Databricks Delta Lakes.
How did I achieve cost reductions and improved processing speed using Delta Tables and how could you as well?
Before digging deep into the methods I used, let us answer this important question:
What is Delta Lake and What are Databricks Delta Tables?
Delta Lake is a technology that makes data storage more reliable and efficient. It provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.
Databricks Delta Tables, built on Delta Lake, offer a modern way to store and process large datasets in the cloud. Integrating seamlessly with Apache Spark, they excel at handling big data for businesses.
Unlike traditional tables, Delta Tables manage changes efficiently, track data history, and support ACID transactions and time travel features, ensuring quick data ingestion and secure storage using the Parquet file format.
If you reached to this point there is two things to keep in mind:
• The primary objectives were to decrease operating expenses, and speed up data processing.
• The data consisted of Key variables like supply quantities, delivery times, and costs which were covered by the client's vast and up-to-date supplier data.
Now let us talk more technical:
Which Methods I used to benefit the most from Databricks Delta Tables?
- Using Delta Tables for Incremental Processing
Solution: This method enhances data pipeline efficiency, ensuring only new or modified data is processed, especially when using Spark and Azure Databricks in cloud computing environments.
For more details, see the article 'Building Incremental Data Pipelines Using Delta Lake'.
- Enhancing Efficiency using Databricks' Caching
Solution: I maintained frequently accessed data immediately available by utilizing Databricks' disk caching capability, which significantly sped up response times.
You can use the following Spark Commands to cache your data
df.cache()#Cache a DataFrame
spark.sql("CACHE TABLE table_name")#Cache a specific table
- Efficient Data Management Using VACUUM
Solution: I routinely eliminated out-of-date data in Delta Lake with the VACUUM command, which helped lower storage costs and increase efficiency.
For more details, see the following Databricks article 'VACUUM best practices on Delta Lake'.
- Enhanced Data Structure via Partitioning
Solution: I restructured the data according to key attributes and reorganized it, which greatly accelerated data access and querying.
Partitioning data by key attributes significantly boosts the performance of real-time analytics and ETL processes in Azure Databricks.
I recommend you to read the Azure Databricks Documentation for this topic 'When to partition tables on Azure Databricks'.
Additional Data Management Techniques you could use
a) File Format Conversion: Switching from slower file formats like CSV to faster ones like Snappy Parquet, improves data processing performance and minimize storage needs.
b) Putting Persist and Repartition Methods into Practice: (Use it if nothing else worked) When dealing with huge datasets you should consider utilizing methods like:
df.persist(StorageLevel.DISK_ONLY)
We can enhance data distribution and persistence in the system using persist techniques and strategic data repartitioning.
c) Column Reordering: One of the essential methods for improving the overall system performance there by reducing query times is to adjust the order of columns in tables to match common query patterns.
Achievements
The notable improvement I achieved which led my client to be the happiest, was the result of using Delta Tables and Databricks optimization techniques. The study showed how important it is to choose the right data engineering tools.
And What do those results mean to my client?
That’s the End of today’s story, I hope you gained new knowledge and/or enjoyed reading.
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