Carlos Godinho Ferreira的动态

?? Just completed the "Knowledge Check: Get Started with Databricks for Data Engineering"! I've confirmed some cool skills on the Databricks platform, like handling data loads, navigating their system, and transforming data efficiently. Really pumped to use these and make data magic happen! Here's to more learning and growing! ?? #Databricks #DataEngineering #LearningJourney

?? What Can We Do with Databricks? ?? Databricks is a unified analytics platform built on Apache Spark, designed to empower Data Engineers, Analysts, and Scientists. Whether you're building data pipelines, analyzing data, or creating machine learning models, Databricks offers the tools and scalability you need to succeed. Here’s how Databricks can elevate your work: #DataEngineering #BigData #ETL #ApacheSpark #CloudComputing #DataAnalytics #DataVisualization #BusinessIntelligence #DataDriven #TechInnovation #DataScience #MachineLearning #PredictiveAnalytics #MLPipelines #ApacheSpark

In today's economy, it's crucial to maximize efficiency and minimize costs with your Databricks clusters. In this article, I'll share some key strategies. Hint: Metrics UI is your best friend for monitoring and optimizing resource usage. #dataengineering #dataengineer #databricks

?? Are you struggling with slow Spark jobs and high processing costs? ?? What if I told you that mastering Directed Acyclic Graphs (DAGs) could be the game-changer you need? ?? Understanding and optimizing your DAGs can unlock significant performance improvements in Azure Databricks. Let’s explore how! ?? ?? Understanding Directed Acyclic Graphs (DAGs) in Spark DAGs represent the sequence of operations that Spark executes to process data. Mastering the DAG is essential for optimizing performance and troubleshooting issues. Here’s a quick guide to help you navigate this crucial aspect of Spark! ?? Step 1: Understanding the DAG ?? Each node in a DAG represents a transformation (like read, filter, aggregate), while edges show data dependencies. Understanding this structure is key to effective data processing! Step 2: Checking the DAG ?? To analyze a specific job: 1. Run Your Job: Execute your notebook as usual. ?? 2. Access the Spark UI: After completion, navigate to the "Spark Jobs" tab in Databricks. ??? 3. View the DAG: Select your job and click on "DAG Visualization" to see stages and execution times. ?? ### Step 3: Analyzing a Problematic DAG ?? If a job runs longer than expected: - Identify Long-Running Stages: Look for stages with significantly longer execution times. ? - Check for Data Skew: Analyze task execution times to identify any partitions handling too much data. ?? - Review Shuffles: Excessive shuffling during joins/aggregations can slow down jobs. Optimize your data partitioning to minimize this. ?? ### Step 4: Improving the DAG ? Once you identify issues, consider: - Optimizing Data Partitioning: Ensure even distribution across partitions. ?? - Caching Intermediate Results: Cache frequently reused results to speed up operations. ?? - Refactoring Complex Transformations: Simplify complex steps to enhance execution efficiency. ?? By following these steps, you can effectively understand, check, and improve DAGs in your Azure Databricks jobs, leading to enhanced performance and efficiency! ???? ?? Let’s discuss! What strategies have you found effective for optimizing DAGs? Have you faced challenges in your Spark jobs that understanding DAGs could help solve? Drop your thoughts in the comments! ?? #AzureDatabricks #Spark #DataEngineering #DAG #PerformanceOptimization #BigData #DataProcessing #MachineLearning #DataAnalytics #CloudComputing

?? Dive into Databricks Lakehouse Platform's Trigger Intervals! ?? ?? Trigger Method Call Behavior: Default: processingTime="500ms" Fixed Interval: .trigger(processingTime="5 minutes") ?? Process data in micro-batches at user-specified intervals. Triggered Batch: .trigger(once=True) ?? Process all available data in a single batch, then stop. Triggered Micro-batches: .trigger(availableNow=True) ?? Process all available data in multiple micro-batches, then stop. ?? Enhance your data stream with: streamDF.writeStream .trigger(processingTime="2 minutes") .outputMode("append") .option("checkpointLocation", "/path") .table(”Output_Table") ?? Boost your data processing efficiency with #DatabricksLakehousePlatform! #StreamlinedWorkflow #DataProcessing

This insightful journey has equipped me with a solid understanding of Databricks, empowering me to streamline data engineering tasks efficiently. I would try to leverage these newfound skills to drive impactful insights in real-world projects! #dataengineering #databricks

???Understanding Data Spilling in Databricks and How to Prevent It If you’re working with big data in Databricks, you might run into an issue called?data spilling. This can slow down your data processing, make things more expensive, and generally lead to a poor experience when running your jobs. But don’t worry! This guide will break down what data spilling is, why it happens, and how you can avoid it. ??What is Data Spilling, and Why Does It Happen? ??Reasons Data Spilling Happens ??How Do I Know if My Data is Spilling? ??How to Stop Data Spilling Read the full article here ?? https://lnkd.in/d6WwTvxk #databricks #dataengineering #dataspilling

?? Simulation of Calculations in Big Data with CROSS JOIN ?? Recently, I worked on a simulation project in Databricks for over 3 million customers, projecting data for the next 5 years. Using CROSS JOIN in Databricks allowed me to combine each customer with a future dates table, enabling projections over any necessary time interval. How It Works: Flexible Date Table: I set the desired interval (in this case, the next 5 years) and reuse the table for all calculations. CROSS JOIN: Combines each customer with all future dates, creating a complete matrix for precise and scalable simulations. Customizable Simulation: This approach enables adjusting the projection period as needed, keeping the code clean and reusable. This technique provided flexibility and accuracy for Big Data projections, making long-term analysis easy to manage. ?? Have you used CROSS JOIN in Databricks for simulations with customizable intervals? Let’s exchange insights! #BigData #Databricks #CROSSJOIN #DataSimulation #DataEngineering #Projection #PySpark #SQL

Thrilled to share my project on Databricks! Tasked with unlocking insights from raw CSV data, I dove into the world of PySpark and SQL, crafting bespoke visualizations along the way. From reshaping messy datasets to fueling business growth, I meticulously cleaned, processed, and analyzed customer and product data to drive strategic KPIs. Explore My Project on Databricks :- https://lnkd.in/dt9qg3mA #DataAnalysis #databrickslearning #databricks #PySparkSQL