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.
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.
On a recent project for one of my customers, a global producer of material handling machinery, my client and I had a big issue to solve. Here is the issue: my client was struggling to efficiently manage & analyze big sets of supplier data; the old system was costly and slow. After checking the old solution applied in Databricks, I came upon an ideal plan* that made my customer happy by reducing costs and boosting our Data Analytics Platform speed.
*My plan was to use contemporary techniques, like Databricks Delta Tables, to enhance the system and its processing speed.
How did I achieve that as a Data Engineer using Delta Tables and how could you as well?
Before digging deep into the methods I used, let us answer this important question:
What are Databricks Delta Tables?
Databricks Delta Tables are a modern way to store and handle lots of data easily in the cloud. They work really well for analyzing big amounts of data, which is great for businesses that want to get the most out of their data. These tables are different from usual ones because they can handle changes and keep track of data history, making it faster to add new data.
Unlike traditional tables that store data in a row and column format, the Databricks Delta Table facilitates ACID transactions and time travel features to store metadata information for quicker Data Ingestion. The data in a Databricks Delta Table is kept in a secure Parquet file format, adding an extra layer of protection to the data.
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
Issue: Too much data was being processed at once by the old method, which was causing delays.
Solution: I used incremental processing using delta tables, so the system only processed new or modified data, greatly accelerating the process.
- Enhancing Efficiency using Databricks' Caching
Issue: The system was being slowed down by repeatedly querying the same data.
Solution: I maintained commonly used data immediately accessible by utilizing Databricks' disk caching capability spark.conf.set("spark.databricks.io.cache.
enabled","true"), which significantly sped up response times.
- Efficient Data Management Using VACUUM
Issue: Outdated, unnecessary data was cluttering the system.
Solution: I routinely eliminated out-of-date data with the VACUUM command, which helped lower storage costs and increase efficiency.
- Enhanced Data Structure via Partitioning
Issue: Data was not arranged for easy access.
Solution: I restructured the data according to key attributes and reorganized it, which greatly accelerated data access and querying.
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) and strategic data repartitioning, where we can enhance data distribution and persistence in the system.
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 dataengineering tools. And What do those results mean to my client?
Hourly processing times were reduced to minutes. Improved data management led to lower operational expenses. Now my client couldn’t be happier while choosing the suppliers more quickly and intelligently.
That’s the End of today’s story, I hope you gained new knowledge and/or enjoyed reading.
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