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Building Real-time Machine Learning Foundations at Lyft

Lyft Engineering

Our goal was to develop foundations that would enable the hundreds of ML developers at Lyft to efficiently develop new models and enhance existing models with streaming data. In this blog post, we will discuss what we built in support of that goal and some of the lessons we learned along the way. register_feature(feature_definition).add_sink(feature_sink)

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Deployment of Exabyte-Backed Big Data Components

LinkedIn Engineering

The new Rolling Upgrade framework The new RU orchestration design significantly enhanced our big data components deployment process. The new orchestrator agent design offers versatility and significantly improves the big data deployment process, making it smoother and less prone to issues.

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How to Join Data in Elasticsearch vs Rockset

Rockset

We will also need to store this data in Elasticsearch. This will allow the front end to pass in the search terms and have the API execute the 3 queries and perform the join before sending the data back to the front end. To do this we will be using NodeJS to build a simple Express API.

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How to Manage Risk with Modern Data Architectures

Cloudera

Design forecasting models that more accurately predict intraday cash flows and liquidity needs. Deliver real-time analytic dashboards, suitable for different stakeholders, that integrate data from payment systems, nostro accounts , internal transactions, and other sources. Enhance counterparty risk assessment.

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Incremental Processing using Netflix Maestro and Apache Iceberg

Netflix Tech

In this blog post, we talk about the landscape and the challenges in workflows at Netflix. The incremental processing solution (IPS) described here has been designed to address the above problems. Downstream workflows (if there is no business logic change) will be triggered by the data change due to backfill.

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Keeping Small Queries Fast – Short query optimizations in Apache Impala

Cloudera

This is part of our series of blog posts on recent enhancements to Impala. Apache Impala is synonymous with high-performance processing of extremely large datasets, but what if our data isn’t huge? It turns out that Apache Impala scales down with data just as well as it scales up. Query Planner Design. Metadata Caching.

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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

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