Remove Aggregated Data Remove Events Remove Metadata
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Keeping Small Queries Fast – Short query optimizations in Apache Impala

Cloudera

The reality is that data warehousing contains a large variety of queries both small and large; there are many circumstances where Impala queries small amounts of data; when end users are iterating on a use case, filtering down to a specific time window, working with dimension tables, or pre-aggregated data.

Metadata 144
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Introducing Netflix TimeSeries Data Abstraction Layer

Netflix Tech

Building on these foundational abstractions, we developed the TimeSeries Abstraction  — a versatile and scalable solution designed to efficiently store and query large volumes of temporal event data with low millisecond latencies, all in a cost-effective manner across various use cases. For example: {“device_type”: “ios”}.

Bytes 96
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Building Real-time Machine Learning Foundations at Lyft

Lyft Engineering

The Event Driven Decisions capability in particular turned out to be general enough as to be applicable to a wide range of use cases. At the time of writing, a Mapping team is working to utilize theEvent Driven Decisions product to rebuild Lyft’s Traffic infrastructure by aggregating data per geohash and applying a model.

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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.

AWS 98
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How to Manage Risk with Modern Data Architectures

Cloudera

Incorporate data from novel sources — social media feeds, alternative credit histories (utility and rental payments), geo-spatial systems, and IoT streams — into liquidity risk models. Apply predictive-analytic and ML techniques to this data to create more accurate profiles and proactively identify high-risk customers.

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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. This metadata includes the namespace, file permissions, and the mapping of data blocks to datanodes.

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The Good and the Bad of Apache Kafka Streaming Platform

AltexSoft

This scenario involves three main characters — publishers, subscribers, and a message or event broker. A publisher (say, telematics or Internet of Medical Things system) produces data units, also called events or messages , and directs them not to consumers but to a middleware platform — a broker. Kafka cluster and brokers.

Kafka 93