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HomeColumnar Databases

Columnar Databases

ClickHouse MergeTree: Understanding Storage Policies and Load Balancing
ClickHouse

ClickHouse MergeTree: Understanding Storage Policies and Load Balancing

ChistaDATA Inc.

Introduction Optimizing data distribution and storage utilization is paramount when working with MergeTree tables in ClickHouse. This involves understanding and leveraging storage policies and load balancing mechanisms to ensure efficient data placement across disks and […]

Migrating from Snowflak
ChistaDATA

Runbook for Migration from Snowflake to ChistaDATA Cloud for ClickHouse: Part 1

ChistaDATA Inc.

Introduction Snowflake is a fully managed SaaS (Software as a service) used for data warehousing, data engineering, data lakes, and data science applications. Snowflake is a proven cloud-based data platform, and it is one of […]

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ChistaDATA

Migrating MSSQL Database to ClickHouse

ChistaDATA Inc.

In the previous article, We discussed migrating from Oracle to the ClickHouse database. This migration technology tool will soon be available in the ChistaDATA DBaaS, and you can enjoy the advantages of ClickHouse database with […]

Get Started with ChistaDATA Cloud for ClickHouse: Part 1
ChistaDATA Cloud

Get Started with ChistaDATA Cloud for ClickHouse: Part 1

ChistaDATA Inc.

Introduction Exciting news for all data enthusiasts! We are thrilled to announce that our ChistaDATA DBaaS platform for ClickHouse is now generally available, providing a powerful and reliable solution for managing and analyzing large volumes […]

ClickHouse Release Notes
ClickHouse

ClickHouse January 2023 Release – Version 23.1

ChistaDATA Inc.

Introduction ClickHouse team is back with a new release for the month of January. Being the first release of the year, it has lot of exciting features, enhancements, and speed improvements. This release has 17 […]

CTO’s Guide to Clickhouse ColumnStores
Columnar Databases

CTO’s Guide to ColumnStores vs Row-based Databases for Real-time Analytics

Shiv Iyer

Introduction Columnar databases, also known as ColumnStores, are a type of database that stores and organizes data by columns rather than by rows. These types of databases are optimized for analytics and reporting workloads, as […]

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* Everything changes over time – Our blogs/posts and comments changes over time, That’s how it should be! Whatever we comment from ChistaDATA Inc. Teams (including Shiv Iyer) and other stakeholders or guest bloggers posted here are never permanent, These things worked for us. But, there is no guarantee they will work for you too, When using the recommendations from ChistaDATA or MinervaDB or MinervaSQL or any other online resources / Google,  You must test the advice before applying them to your production systems, and always invest for a robust Database DR solution, Thank you for understanding. 

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Contents

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  • Introduction
  • How is data stored in a ColumnStore?
  • Why are ColumnStores faster for Data Analytics than Row-based DBs?
    • How Vectorized Query Processing benefits Columnar Databases?
    • C++ algorithm to implement vectorized query computing
    • How ColumnStores compress data more efficiently than row-based database systems?
    • Why are B-Tree indexes not recommended for Analytical Query Processing (OLAP) Platforms?
  • Why use ClickHouse for real-time Data Analytics?
    • How does a sparse index in ClickHouse benefit query performance?
  • How can ChistaDATA help you build web-scale real-time data analytics using ClickHouse?
    • Why do successful companies work with ChistaDATA for ClickHouse Support and Managed Services?
  • Conclusion
→ Index