Remove Events Remove Metadata Remove Raw Data
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Metadata: What Is It and Why it Matters

Ascend.io

Metadata is the information that provides context and meaning to data, ensuring it’s easily discoverable, organized, and actionable. It enhances data quality, governance, and automation, transforming raw data into valuable insights. This is what managing data without metadata feels like.

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Interesting startup idea: benchmarking cloud platform pricing

The Pragmatic Engineer

Benchmarking: for new server types identified – or ones that need an updated benchmark executed to avoid data becoming stale – those instances have a benchmark started on them. Results are stored in git and their database, together with benchmarking metadata. Then we wait for the actual data and/or final metadata (e.g.

Cloud 273
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Databricks, Snowflake and the future

Christophe Blefari

Below a diagram describing what I think schematises data platforms: Data storage — you need to store data in an efficient manner, interoperable, from the fresh to the old one, with the metadata. It adds metadata, read, write and transactions that allow you to treat a Parquet file as a table.

Metadata 147
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1. Streamlining Membership Data Engineering at Netflix with Psyberg

Netflix Tech

Types of late-arriving data Based on the structure of our upstream systems, we’ve classified late-arriving data into two categories, each named after the timestamps of the updated partition: Ways to process such data Our team previously employed some strategies to manage these scenarios, which often led to unnecessarily reprocessing unchanged data.

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Solving Data Lineage Tracking And Data Discovery At WeWork

Data Engineering Podcast

The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools.

Metadata 100
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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

ProjectPro

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. Why Use AWS Glue?

AWS 98
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Building a Data Platform in 2024

Towards Data Science

In truth, the synergy between batch and streaming pipelines is essential for tackling the diverse challenges posed to your data platform at scale. The key to seamlessly addressing these challenges lies, unsurprisingly, in data orchestration. This metadata is then utilized to manage, monitor, and foster the growth of the platform.