ClickHouse DBA Support Archives | Page 10 of 23

Understanding Clickhouse Advanced Hash and Merge Joins

ClickHouse JOIN: Understanding Advanced Hash and Merge Joins

Introduction In the realm of data analytics, performance is often the key differentiator between success and stagnation. ClickHouse, an open-source columnar database management system, has gained popularity for its exceptional query performance, especially when it […]

Overview of Vectorized Query Processing in ClickHouse

Vectorized Query Processing for ClickHouse Performance

Shiv Iyer

Introduction: Vectorized query computing is a crucial optimization technique that significantly influences the performance of real-time analytics solutions. It transforms the way queries are processed by simultaneously operating on multiple data elements, maximizing hardware resources […]

ClickHouse JOIN: Deep Dive into Nested Loop and Merge Scan Joins

Shiv Iyer

Introduction Choosing the right join strategy is crucial for optimizing query performance in ClickHouse. ClickHouse supports two main types of joins: Nested-Loop Join and Merge-Scan Join. Let’s delve into each join type, explain how they […]

5 Reasons to use ChistaDATA’s ClickHouse for Real-time Analytics

Shiv Iyer

Introduction As technology leaders, understanding the intricacies of data management systems is crucial for making informed decisions. ClickHouse, an open source columnar database management system, offers a unique perspective on data analytics, especially in the […]

ClickHouse Data Compression Techniques for Time-series Datasets

Shiv Iyer

Introduction ClickHouse, a powerful columnar database management system, employs various techniques to optimize data storage and retrieval. Among these techniques, delta-encoding and dictionary encoding play a pivotal role in enhancing compression, particularly for time-series and […]

Comparing Columnar vs Row-based Databases for Real-time Analytics

Shiv Iyer

Introduction In the dynamic landscape of data management, the choice between columnar stores and ROW-based databases has a profound impact on performance, especially in scenarios requiring rapid data retrieval and analytical prowess. These two approaches, though fundamentally […]

Real-time Analytics in ClickHouse for Risk Management in Banking

Real-time Analytics with ClickHouse for Risk Management in Banking

Shiv Iyer

Introduction In the dynamic realm of consumer banking, where transactions happen at lightning speed and risks lurk around every corner, the ability to predict and manage these risks in real time is not just an […]

Data Compression in ClickHouse vs Cassandra

Shiv Iyer

Introduction ClickHouse and Cassandra: two different databases optimized for different use cases. ClickHouse is a columnar database designed for analytical queries, while Cassandra is a NoSQL database designed for write-heavy workloads and scalability. Because of these fundamental differences in design and use […]

Cassandra’s Indexing Limitations for Real-time Analytics

Shiv Iyer

Introduction Cassandra, a highly-scalable NoSQL database, is renowned for its write-optimized architecture and resilience. With its capacity to handle vast volumes of data across distributed systems, many consider it a go-to choice for various applications. […]

ClickHouse vs Hadoop for Real-time Analytics

Shiv Iyer

Introduction This article highlights why Hadoop may not be the best fit for real-time analytics and discusses how ClickHouse emerges as a more suitable solution. 10 Reasons Why Hadoop is Not Recommended for Real-Time Analytics: […]

* 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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