Description
Adobe’s Unified Profile System is the heart of its Experience Platform. It ingests TBs of data a day and is PBs large. As part of this massive growth we have faced multiple challenges in our Apache Spark deployment which is used from Ingestion to Processing. We want to share some of our learnings and hard earned lessons and as we reached this scale specifically with Structured Streaming. Know thy Lag While consuming off a Kafka topic which sees sporadic loads, its very important to monitor the Consumer lag. Also makes you respect what a beast backpressure is. Reading Data In Fan Out Pattern using minPartitions to Use Kafka Efficiently Overload protection using maxOffsetsPerTrigger More Apache Spark Settings used to optimize Throughput MicroBatching Best Practices Map() +ForEach() vs MapPartitons + forEachPartition Adobe Spark Speculation and its Effects Calculating Streaming Statistics Windowing Importance of the State Store RocksDB FTW Broadcast joins Custom Aggegators OffHeap Counters using Redis Pipelining Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedi…
Description from YouTube. Full content on the video page.
More from Databricks
NewsHow adidas Uses Databricks to Build Better Products
Adidas uses Databricks' lakehouse platform to centralize all its data—from product to football-related insights—enabling faster analytics across the organization. The company's Genie analytics tool helps analysts spend less time processing data and more time on strategic questions, ultimately supporting better product development.
NewsDatabricks for Good x MapAid: Creating a Searchable Database for Groundwater Discovery
MapAid is using Databricks to build an AI groundwater mapping system called Well Mapper that processes water documents to identify accurate well-drilling locations in Ethiopia, improving on the current 30% success rate. The system analyzes 400 well logs in half a second instead of four weeks, with potential to double Ethiopia's food supply through improved irrigation.
NewsHow Databricks Genie Automates Data Workflows with Genie Ontology and Scheduled Tasks
Databricks Genie enables ontology by default for business context and adds document/PDF uploads, direct Unity Catalog queries, and team collaboration features in chat. Scheduled tasks automate recurring workflows with embedded visualizations and PDF outputs accessible across web, desktop, and mobile platforms.
NewsGenie One Beginner's Guide: Explore Data & Automate Tasks
Genie One helps subject matter experts avoid repetitive questions by using business ontology to understand your data and run real-time queries that generate instant answers and shareable reports. The tool can also automate monitoring with conditional alerts that only notify you when specific business thresholds are met.
TutorialsHow to Schedule Automated Meeting Prep in Genie One
Genie One allows users to connect multiple data sources like Databricks tables and Google Calendar, then create analyses through natural language prompts to prepare for customer meetings. The platform can schedule these analyses to run automatically on a recurring basis and deliver results via email or mobile app.
TutorialsHow to Build Custom Skills in Genie One in Minutes
Genie 1 can populate business report templates by analyzing their format and running SQL queries against organizational data. Users can save these report workflows as reusable skills that execute with a single slash command, enabling automation of recurring reports.