Remove Aggregated Data Remove Events Remove Metadata Remove Systems
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Deployment of Exabyte-Backed Big Data Components

LinkedIn Engineering

Our RU framework ensures that our big data infrastructure, which consists of over 55,000 hosts and 20 clusters holding exabytes of data, is deployed and updated smoothly by minimizing downtime and avoiding performance degradation. During cluster degradations, the framework auto-pauses and resumes, mitigating potential intricacies.

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Building Real-time Machine Learning Foundations at Lyft

Lyft Engineering

In early 2022, Lyft already had a comprehensive Machine Learning Platform called LyftLearn composed of model serving , training , CI/CD, feature serving , and model monitoring systems. However, streaming data was not supported as a first-class citizen across many of the platform’s systems — such as training, complex monitoring, and others.

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How to Manage Risk with Modern Data Architectures

Cloudera

To ensure the stability of the US financial system, the implementation of advanced liquidity risk models and stress testing using (MI/AI) could potentially serve as a protective measure. Use cases include: Enable transparent access to financial data. Possible applications include: Improved customer risk profiling.

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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

Application programming interfaces (APIs) are used to modify the retrieved data set for integration and to support users in keeping track of all the jobs. Users can schedule ETL jobs, and they can also choose the events that will trigger them. Then, Glue writes the job's metadata into the embedded AWS Glue Data Catalog.

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Evolution of Streaming Pipelines in Lyft’s Marketplace

Lyft Engineering

The team needed better infrastructure to make the dynamic pricing system more reactive for the following reasons: Decrease end-to-end latency that would make the system more reactive to marketplace imbalances. This pipeline ingests tens of millions of events per second and processes them into machine learning features.

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Internal services pipeline in Analytics Platform

Picnic Engineering

Almost all internal services emit events over RabbitMQ. Our pipeline captures these events and sends them to Confluent Cloud. Now we are going to take a deeper look into each sub-part of our system. RabbitMQ We have already mentioned that RabbitMQ is used as the main inter-service communication event bus at Picnic.

Kafka 52
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ELT Process: Key Components, Benefits, and Tools to Build ELT Pipelines

AltexSoft

ELT is now gaining popularity as an alternative to a traditional ETL (Extract, Transform, Load) process, in which the transformation phase occurs before the data is loaded into a target system. One of the main reasons behind this is the need to timely process huge volumes of data in any format. ELT vs ETL. Scalability.

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