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Showing posts from February, 2025

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

How to Configure a Databricks Cluster to Process 10 TB of Data Efficiently ๐Ÿš€ 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. ๐Ÿ“Œ 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 ๐Ÿ’ก Tip: Use file formats like Parquet or Delta Lake to ensure partitions are splittable. ๐Ÿ“Œ Step 2: Determine Number of Nodes Assuming each node handles 100–200 partitions effectively: Without overhead: 80,000 / 100–200 = 400 to 800...

Common Key Terms & Terminologies

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 scroll down or do CTRL + F if you don't find any term on top........................................ Data warehouse A Data Warehouse (DWH) is a centralized repository designed for storing, managing, and analyzing large volumes of structured data from multiple sources. It enables businesses to perform complex queries, generate reports, and gain insights for decision-making. Key Characteristics: Subject-Oriented : Organized around key business areas (e.g., sales, finance). Integrated : Combines data from different sources into a unified format. Time-Variant : Stores historical data for trend analysis. Non-Volatile : Data is read-only and does not change once stored. Common Technologies: On-Premise : SQL Server, Oracle, Teradata Cloud-Based : Amazon Redshift , Google BigQuery, Snowflake, Azure Synapse Analytics A data warehouse supports Business Intelligence (BI) and analytics by providing structured, cleaned, and optimized data for reporting and decision-making.

Amazon Redshift

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                                                      Amazon Redshift 

Insight of Alteryx

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organizations are faced with an ever-increasing volume of data from diverse sources. The ability to harness, process, and analyze this data is paramount to making informed decisions and gaining a competitive edge. Alteryx, a data analytics platform, has emerged as a powerful tool for transforming raw data into actionable insights. In this article, we will explore what Alteryx is, its key features, and how it empowers businesses to unlock the potential of their data. What is Alteryx? Alteryx is a data analytics platform that offers a comprehensive set of tools for data blending, preparation, and advanced analytics. It provides a user-friendly, code-free environment for data professionals to work with data, enabling them to perform complex data operations, create predictive models, and deliver valuable insights. Key Features of Alteryx Workflow : At the core of Alteryx is the concept of a workflow, which represents a sequence of connected tools that perform specific data operations. Wor...