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Upgrade your Modern Data Stack

Christophe Blefari

We jumped from HDFS to Cloud Storage (S3, GCS) for storage and from Hadoop, Spark to Cloud warehouses (Redshift, BigQuery, Snowflake) for processing. Historically, data pipelines were designed with an ETL approach, storage was expensive and we had to transform the data before using it. Is the modern data stack dying?

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How DoorDash Migrated from StatsD to Prometheus

DoorDash Engineering

Challenges Faced With StatsD StatsD was a great asset for our early observability needs, but we began encountering constraints such as losing metrics during surge events, difficulties with naming/standardized tags, and a lack of reporting tools. These common tags are useful to create common dashboards and alerts to monitor service health.

AWS 82
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Introducing Vector Search on Rockset: How to run semantic search with OpenAI and Rockset

Rockset

Before vector search, search experiences primarily relied on keyword search, which frequently involved manually tagging data to identify and deliver relevant results. As an example, if we wanted to search for tagged keywords to deliver product results, we would need to manually tag “Fortnite” as a ”survival game” and ”multiplayer game.”

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How to get started with dbt

Christophe Blefari

In terms of paradigms before 2012 we were doing ETL because storage was expensive, so it became a requirement to transform data before the data storage—mainly a data warehouse, to have the most optimised data for querying. It was the previous tag line dbt Labs had on their website. With the public clouds—e.g.

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Complying with Quebec’s Data Privacy Laws Is Easier with the Data Cloud

Snowflake

This is made easier if PII data was appropriately classified and tagged as part of the privacy impact assessment, and so this is a best practice for organizations to follow. Customers can classify and tag PII through Snowflake features to track where that data is and ensure policies are in place to protect it.

Cloud 76
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Building Netflix’s Distributed Tracing Infrastructure

Netflix Tech

If we had an ID for each streaming session then distributed tracing could easily reconstruct session failure by providing service topology, retry and error tags, and latency measurements for all service calls. Our distributed tracing infrastructure is grouped into three sections: tracer library instrumentation, stream processing, and storage.

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. This structure is made efficient by data engineering practices that include object storage. Many organizations also deploy data marts , which are dedicated storage repositories for specific business lines or workgroups.