How to Configure a Databricks Cluster to Process 10 TB of Data Efficiently

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How to Configure a Databricks Cluster to Process 10 TB of Data Efficiently 🚀 Sizing a Databricks Cluster for 10 TB: A Step-by-Step Optimization Guide Processing 10 TB of data in Databricks may sound intimidating, but with a smart cluster sizing strategy, it can be both fast and cost-effective . In this post, we’ll walk through how to determine the right number of partitions, nodes, executors, and memory to optimize Spark performance for large-scale workloads. 📌 Step 1: Estimate the Number of Partitions To unlock Spark’s parallelism, data must be split into manageable partitions . Data Volume: 10 TB = 10,240 GB Target Partition Size: ~128 MB (0.128 GB) Formula: 10,240 / 0.128 = ~80,000 partitions 💡 Tip: Use file formats like Parquet or Delta Lake to ensure partitions are splittable. 📌 Step 2: Determine Number of Nodes Assuming each node handles 100–200 partitions effectively: Without overhead: 80,000 / 100–200 = 400 to 800...
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                                             Data Modelling - Star vs Snowflake Schema!!


Today, we'll dive into data modeling concepts, specifically focusing on star and snowflake schemas. 

 In a star schema, we have a central fact table surrounded by dimension tables. The fact table contains quantitative data, usually numerical metrics or measures, while the dimension tables contain descriptive attributes that provide context to the measures. The fact table is connected to the dimension tables through foreign key relationships, forming a star-like shape.

In a snowflake schema, the dimension tables are normalized, meaning that they are further broken down into multiple related tables. This results in a more complex network of relationships, resembling the branches of a snowflake. While this normalization can save storage space and reduce data redundancy, it can also lead to increased query complexity due to the need for additional joins.

Q In what scenarios would you prefer using a snowflake schema over a star schema, and vice versa?"

"Choosing between a star and snowflake schema depends on various factors such as the nature of the data, query patterns, and performance requirements. A star schema is simpler and easier to understand, making it suitable for scenarios where performance and simplicity are prioritized. On the other hand, a snowflake schema may be preferred in scenarios where data integrity and storage optimization are critical, and the additional complexity introduced by normalization is acceptable."

Now, let's consider a hypothetical scenario where you're tasked with designing a data warehouse for an e-commerce company. Would you opt for a star or snowflake schema, and why?"

"In the case of an e-commerce company, where performance and ease of querying are paramount, I would lean towards a star schema. The simplicity and denormalization of the star schema would facilitate efficient querying of sales data and analytics. However, I would consider normalizing certain dimension tables in a snowflake-like fashion if there are large, frequently updated attributes that could benefit from reduced redundancy and improved data integrity."

Hope it helps!


#datamodel #starschema #snowflake #datawarehouse

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