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Druid Deprecation and ClickHouse Adoption at Lyft

Lyft Engineering

Introduction At Lyft, we have used systems like Apache ClickHouse and Apache Druid for near real-time and sub-second analytics. Sub-second query systems allow for near real-time data explorations and low latency, high throughput queries, which are particularly well-suited for handling time-series data.

Kafka 104
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A Breakthrough Architecture for Real-Time Analytics- An Overview of Compute-Compute Separation in Rockset

Rockset

Rockset introduces a new architecture that enables separate virtual instances to isolate streaming ingestion from queries and one application from another. Benefits of Compute-Compute Separation In this new architecture, virtual instances contain the compute and memory needed for streaming ingest and queries.

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Data Pipeline- Definition, Architecture, Examples, and Use Cases

ProjectPro

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. As data is expanding exponentially, organizations struggle to harness digital information's power for different business use cases. What is a Big Data Pipeline?

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Comparing ClickHouse vs Rockset for Event and CDC Streams

Rockset

Change data capture (CDC) streams from OLTP databases, which may provide sales, demographic or inventory data, are another valuable source of data for real-time analytics use cases. Architecture ClickHouse was developed, beginning in 2008, to handle web analytics use cases at Yandex in Russia. Flink, Kafka and MySQL.

MySQL 52
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What Is a Data Mesh?

Ascend.io

Data represents our present and our future, and therein lies a significant problem: the more data you’re dealing with, the more challenging it will be to scale your company in a sustainable and standardized way. It provides a more distributed, decentralized, and resilient approach to data management. So, what’s the solution?

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What Is a Data Mesh?

Ascend.io

Data represents our present and our future, and therein lies a significant problem: the more data you’re dealing with, the more challenging it will be to scale your company in a sustainable and standardized way. It provides a more distributed, decentralized, and resilient approach to data management. So, what’s the solution?

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Tips to Build a Robust Data Lake Infrastructure

DareData

Understanding the Architecture No company is alike and no infrastructure will be alike. Although there are some guidelines that you can follow when setting up a data infrastructure, each company has it's own needs, processes and organizational structure. Data Sources: How different are your data sources?