Building a Streaming Microservice Architecture: with Apache Spark Structured Streaming and Friends
Description
As we continue to push the boundaries of what is possible with respect to pipeline throughput and data serving tiers, new methodologies and techniques continue to emerge to handle larger and larger workloads – from real-time processing and aggregation of user / behavioral data, rule-based / conditional distribution of event and metric streams, to almost any data pipeline / lineage problems. These workloads are typical in most modern data platforms and are critical to all operational analytics systems, data storage systems, ML / DL and beyond. One of the common problems I’ve seen across a lot of companies can be reduced to general data reliability problems. Mainly due to scaling and migrating processing components as a company expands and teams grow. What was a few systems can quickly fan out into a slew of independent components and serving-layers all whom need to be scaled up, down or out with zero-downtime to meet the demands of a world hungry for data. During this technical deep dive, a new mental model will be built up which aims to reinvent how one should build massive, interconnected services using Kafka, Google Protocol Buffers / gRPC, and Parquet/Delta Lake/Spark Structured…
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.