Aggregated Data Blog Building Metadata 
Lyft Engineering
JUNE 28, 2023
While several teams were using streaming data in their Machine Learning (ML) workflows, doing so was a laborious process, sometimes requiring weeks or months of engineering effort. On the flip side, there was a substantial appetite to build real-time ML systems from developers at Lyft.
Netflix Tech
NOVEMBER 20, 2023
In this blog post, we talk about the landscape and the challenges in workflows at Netflix. We will show how we are building a clean and efficient incremental processing solution (IPS) by using Netflix Maestro and Apache Iceberg. data arrives too late to be useful). It works well to backfill data produced by a single workflow.
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LinkedIn Engineering
DECEMBER 19, 2023
Figure 3: Generalized rolling upgrade deployment flow Namenode deployment overview The namenode is the central component of HDFS and is responsible for storing the metadata information about files and directories in the HDFS cluster. This metadata includes the namespace, file permissions, and the mapping of data blocks to datanodes.
ProjectPro
FEBRUARY 8, 2023
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.
DoorDash Engineering
APRIL 12, 2023
Building a metrics layer that works for experimentation is not simple, as it should support different types of metrics of varying scale that are used across the diverse range of A/B tests that are being run across different products. We will also dive deep into our design and implementation processes and the lessons we learnt.
Netflix Tech
APRIL 26, 2022
We will share how its design has evolved over the years and the lessons learned while building it. An example of data about members is the video they had watched or added to their My List. An example of video data is video metadata, like the length of a video. Our machine learning models train on several weeks of data.
Lyft Engineering
SEPTEMBER 27, 2022
To build such pipelines, we decomposed the feature generation pipeline into two (see Figure 4). The first type of pipeline was mainly for event ingestion, filtration, hydration, and metadata tagging. The second type of pipeline ingests Kafka topics and aggregates data into standard ML features.
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