ChistaDATA Performance Archives | Page 2 of 6

Real-time Analytics in Modern Banking with ChistaDATA’s ClickHouse

Introduction: Real-time Analytics in Modern Banking In the fast-paced world of modern banking, making accurate and timely decisions is crucial for success. Traditional data analysis methods are no longer sufficient to keep up with the […]

Inside the ClickHouse Query Execution Pipeline

ClickHouse Query Execution Pipeline: A Look Inside

Shiv Iyer

Introduction When it comes to high-performance analytics, ClickHouse stands out as a powerful columnar database. Behind its blazing-fast query processing lies a sophisticated execution pipeline that optimizes query performance and enables efficient data retrieval. In […]

ChistaDATA Cloud for ClickHouse v/s Google BigQuery: Comparative Benchmarking

ChistaDATA Inc.

Introduction BigQuery is Google’s fully managed data warehouse which is offered with its own SQL dialect support. BigQuery was launched in the year 2011 and has been a mature cloud offering ever since. BigQuery is […]

Configuring ClickHouse Server for High Performance

How to Configure ClickHouse Server for High Performance

Shiv Iyer

Introduction Configuring the ClickHouse Servercan significantly impact the performance of your queries. While this may not affect the performance of ClickHouse systems with smaller datasets, it significantly impacts the performance of datasets at scale with […]

multi-region ClickHouse deployment architecture showing distributed cluster replication : Sharding and Resharding Strategies

ClickHouse Horizontal Scaling: Sharding and Resharding Strategies

ChistaDATA Inc.

Introduction: Sharding and Resharding Strategies Sharding is a process in which a large database table is divided horizontally into smaller ones (with same schema/columns) and stored across different nodes. ClickHouse supports sharding via distributed table […]

ChistaDATA's Anansi Query Profiler for ClickHouse

ChistaDATA Anansi Query Profiler

ChistaDATA Inc.

Introduction Top-n queries extract the top or bottom n rows from a result set. In other words, they identify the best or worst examples, such as the top 10 place in a particular area, the […]

Replicating Data across ClickHouse Servers

clickhouse-copier – A reliable workhorse for copying data across ClickHouse servers

ChistaDATA Inc.

Introduction ClickHouse comes with useful tools for performing various tasks. clickhouse-copier is one among them and as the name suggests, it is used for copying data from one ClickHouse server to another. The servers can […]

How to perform a full-text phrase search in ClickHouse?

Shiv Iyer

To perform full-text phrase search in ClickHouse, you can use the match() function in combination with regular expressions. Although ClickHouse does not have a built-in full-text search feature like some other databases, the match() function […]

Implementing JOINS in ClickHouse for High-Performance Real-Time Analytics

Shiv Iyer

Introduction In ClickHouse, joins can significantly improve performance when working with large datasets. Joins allow you to combine data from multiple tables based on a common key, and perform various operations on the resulting combined […]

Streaming Data from PostgreSQL to ClickHouse using Kafka and Debezium: Part 1

ChistaDATA Inc.

Introduction A quick calculation of analytical business data using metrics for modeling, planning, or forecasting is possible with OLAP only. Also a lot of business applications for reporting, simulation models, information-to-knowledge transfers, and trend and […]

* 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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