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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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Optimizing SQL queries

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  🚀 Optimizing SQL queries is crucial for improving database performance and ensuring efficient use of resources. 👉 Few SQL query optimization techniques are as below: ✅ Index Optimization ➡️ Ensure indexes are created on columns that are frequently used in 'WHERE' clauses, 'JOIN' conditions and as part of 'ORDER BY' clauses. ➡️Use composite indexes for columns that are frequently queried together. ➡️Regularly analyze and rebuild fragmented indexes. ✅ Query Refactoring ➡️ Break complex queries into simpler subqueries or use common table expressions (CTEs). ➡️ Avoid unnecessary columns in the 'SELECT' clause to reduce the data processed. ✅ Join Optimization ➡️ Use the appropriate type of join (INNER JOIN, LEFT JOIN, etc.) based on the requirements. ➡️ Ensure join columns are indexed to speed up the join operation. ➡️ Consider the join order, starting with the smallest table. ✅ Use of Proper Data Types ➡️ Choose the most efficient data type for your col...
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