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Tag: Spark

  • Databricks Optimization Techniques for Enhanced Performance

    Let’s dive into some key Databricks optimization techniques to enhance the performance and efficiency of your data processing workloads. These techniques span various aspects of the Databricks platform and Apache Spark. 1. Data Partitioning Concept: Dividing your data into smaller, more manageable chunks based on the values of one or more columns. This allows Spark… Read more

  • Databricks Workflow Sample: Simple ETL Pipeline

    Let’s walk through a sample Databricks Workflow using the Workflows UI. This example will demonstrate a simple ETL (Extract, Transform, Load) pipeline: Scenario: Extract: Read raw customer data from a CSV file in cloud storage (e.g., S3, ADLS Gen2). Transform: Clean and transform the data using a Databricks notebook (e.g., filter out invalid records, standardize… Read more

  • Databricks Data Ingestion Samples

    Let’s explore some common Databricks data ingestion scenarios with code samples in PySpark (which is the primary language for data manipulation in Databricks notebooks). Before You Begin Set up your environment: Ensure you have a Databricks workspace and have attached a notebook to a running cluster. Configure access: Depending on the data source, you might… Read more

  • Databricks High level Concepts

    Databricks High-Level Concepts: A Detailed Overview Databricks High-Level Concepts: A Detailed Overview Databricks is a unified analytics platform built on top of Apache Spark, designed to simplify big data processing and machine learning. It provides a collaborative environment for data scientists, data engineers, and business analysts. Here’s a detailed overview of its key high-level concepts:… Read more

  • Parquet “Indexing”

    While Parquet itself doesn’t have traditional database-style indexes that you explicitly create and manage, it leverages its columnar format and metadata to optimize data retrieval, which can be considered a form of implicit indexing. When it comes to joins, Parquet’s efficiency can significantly impact join performance in data processing frameworks. Here’s a breakdown of Parquet… Read more

  • Broadcast Hash Join

    The Broadcast Hash Join is a join optimization strategy used in distributed data processing frameworks like Apache Spark, Dask, and others. It’s particularly effective when one of the tables being joined is significantly smaller than the other and can fit into the memory of each executor node in the cluster. Here’s how it works: Algorithm:… Read more

  • Detail of Parquet

    The Parquet format is a column-oriented data storage format designed for efficient data storage and retrieval. It is an open-source project within the Apache Hadoop ecosystem. Here’s a breakdown of its key aspects: Key Characteristics: Advantages of Using Parquet: Disadvantages of Using Parquet: Parquet vs. Other Data Formats: In summary, Parquet is a powerful and… Read more

  • Simplistic implementation of Medallion Architecture (With Code)

    Here we demonstrate a simplistic implementation of Medallion Architecture. Medallion Architecture provides a structured and robust approach to building a data lakehouse. By progressively refining data through the Bronze, Silver, and Gold layers, organizations can ensure data quality, improve governance, and ultimately derive more valuable insights for their business Python Explanation of the Sample Code… Read more

  • Databricks scalability

    Databricks is designed with scalability as a core tenet, allowing users to handle massive amounts of data and complex analytical workloads. Its scalability stems from several key architectural components and features: 1. Apache Spark as the Underlying Engine: 2. Decoupled Storage and Compute: 3. Elastic Compute Clusters: 4. Auto Scaling: 5. Serverless Options: 6. Optimized… Read more