ClickHouse DBA Support Archives | Page 13 of 26

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

Comparing Columnar Databases vs Row-based Databases

Comparing Columnar Databases vs Row-based Databases 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. […]

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

Cassandra 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: […]

ClickHouse vs Cassandra for Real-time Analytics

Shiv Iyer

Introduction ClickHouse and Cassandra are both powerful data management systems, but they are designed for different use cases. 1. Data Model 2. Query Language 3. Aggregation 4. Data Compression 5. Indices 6. Throughput Conclusion The […]

ClickHouse Performance: Mastering ClickHouse Thread Tuning

Shiv Iyer

Introduction ClickHouse is renowned for its high-speed data processing capabilities, making it a popular choice for real-time analytics and big data applications. To extract the maximum potential from ClickHouse, optimizing thread performance is crucial. Thread […]

Implementing Branching on ChistaDATA Cloud for ClickHouse

ChistaDATA Inc.

Introduction In the world of modern software development, managing data efficiently and effectively is crucial for maintaining a successful and scalable application. One of the techniques that has gained popularity in recent years is […]

🚀 Exciting Plans for MinervaDB Inc. and ChistaDATA Inc.! 🌟

Shiv Iyer

Empowering Data Management: The Vision for MinervaDB and ChistaDATA As the founder of MinervaDB Inc. and ChistaDATA Inc., I am thrilled to share our vision for the next two years. Our companies have been established […]

ClickHouse Performance: Real-Time Monitoring of Expensive Queries

Shiv Iyer

Introduction Expensive queries in ClickHouse can have a significant impact on overall system performance, consuming valuable resources like CPU, memory, and disk I/O. These resource-intensive queries can lead to increased latency and reduced throughput, hampering […]

* 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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