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Blog

ClickHouse Horizontal Scaling: Optimal Read-Write Split Configuration and Execution
ClickHouse

Real-Time Bid Tracking and Optimization with ClickHouse in High-Performance Data Pipelines

Shiv Iyer
Optimizing Real-Time Bidding Efficiency: Harnessing ClickHouse for Advanced Data Pipeline Management In the rapidly evolving landscape of real-time bidding (RTB) platforms, the ability to process and analyze large volumes of data at high speeds is […]
Application Domain Index for Nested Data Structures
ChistaDATA

Optimizing Revenue for FinTech Platforms Using K-Means Algorithms in Google BigQuery

Shiv Iyer
Harnessing the sophisticated K-Means algorithm within Google BigQuery’s comprehensive analytics ecosystem presents a transformative opportunity to substantially enhance revenue optimization strategies for FinTech platforms. This advanced machine learning technique demonstrates remarkable proficiency in categorizing diverse […]
Machine Learning in ClickHouse
ClickHouse

Understanding ClickHouse MergeTree: Data Organization, Merging, Replication, and Mutations Explained

Shiv Iyer
Understanding ClickHouse MergeTree: Data Organization, Merging, Replication, and Mutations Explained ClickHouse is renowned for its high-performance analytics and its ability to efficiently handle massive amounts of data. At the core of ClickHouse’s data storage and […]
Tuning Linux for ClickHouse Performance
ClickHouse Performance

Delta Updates in OLAP Databases: Why They Hurt Performance

Shiv Iyer
Why Delta Updates Are Not Recommended in OLAP Databases: A Performance and Efficiency Perspective Delta Updates in OLAP (Online Analytical Processing) databases are not recommended due to the fundamental design and architecture of these systems, […]
ClickHouse Search: Manticore Full Text Search with Plain Index
ClickHouse Security

Mastering User Management in ClickHouse: A Complete Guide to Authentication, Authorization, and Future Security Enhancements

Shiv Iyer
User Management in ClickHouse: A Comprehensive Guide Introduction User management is a critical aspect of any analytical application, as it ensures secure access to data while maintaining flexibility for various users. In ClickHouse, user management […]
ClickHouse Performance

ClickHouse Parquet Ingestion via Kafka: A Step-by-Step Guide

Shiv Iyer
Unlock the Power of Data: Seamlessly Integrate Parquet File Ingestion into ClickHouse with Kafka – Your Ultimate Step-by-Step Guide to Optimized Performance! ClickHouse Parquet Ingestion enables a seamless, high-performance workflow for moving data from distributed […]
ClickHouse Data Compression Techniques for Time-series Datasets
ClickHouse

Optimizing Non-SARGable Predicates in ClickHouse for Improved Query Performance

Shiv Iyer
ClickHouse Query Optimization Non-SARGable (Search ARGument ABLE) predicates are conditions in SQL queries that prevent the database engine from using indexes efficiently, leading to full table scans and degraded query performance. Implementing and handling Non-SARGable […]
We explore how the data is migrated from Kafka to ClickHouse database using Vector tool. We can also use the same tool for Nginx and K8s.
ClickHouse

ClickHouse July 2024 Release – v24.6

ChistaDATA Inc.
Introduction Every new release includes new features, enhancements, and numerous bug fixes, and the ChistaDATA team always stays on top of the latest releases. On July, 2024, ClickHouse version 24.6 was released, and this version […]
ChistaDATA

ChatGPT Database Integration with ChistaDATA DBaaS: Part 2

ChistaDATA Inc.
In our previous blog, we explored the transformative role of generative AI in analytics and highlighted the benefits of using ClickHouse for high-performance data processing. Now, we are now thrilled to take a significant leap […]
Tuning ClickHouse for High-Velocity Data Ingestion in Distributed Tables
ClickHouse Performance

ClickHouse S3 Tiered Storage for Data Archival and Compliance

Shiv Iyer
ClickHouse S3 Archival is a common strategy for handling large volumes of data efficiently, particularly for compliance purposes where data must be retained but is queried infrequently. Using tiered storage like S3 for archiving data […]

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ChistaDATA is committed to open source software and building high performance ColumnStores

In the spirit of freedom, independence and innovation. ChistaDATA Corporation is not affiliated with ClickHouse Corporation 

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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. 

Recent Posts from ChistaDATA

  • ClickHouse BYOC with ChistaDATA: The Complete 2026 Architecture Guide
  • How to Read system.parts to Diagnose ClickHouse Storage and Performance Issues
  • ClickHouse EXPLAIN PIPELINE: A Proven 7-Step Guide to Decoding Query Execution Bottlenecks
  • Memory Limit Exceeded in ClickHouse: Root Causes and Permanent Fixes
  • Troubleshooting High CPU Usage in ClickHouse: From system.trace_log to Flamegraphs

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We are committed to building Optimal, Scalable, Highly Available, Reliable, Fault-Tolerant and Secured Database Infrastructure Operations for WebScale to our customers globally

CHISTADATA IS COMMITTED TO OPEN SOURCE SOFTWARE AND BUILDING HIGH PERFORMANCE COLUMNSTORES

In the spirit of freedom, independence and innovation. ChistaDATA Corporation is not affiliated with ClickHouse Corporation 

ChistaDATA Inc. Knowledge base is licensed under the Apache License, Version 2.0 (the “License”)

Copyright 2022 ChistaDATA Inc

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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