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HomeAuthorsShiv Iyer

Articles by Shiv Iyer

About Shiv Iyer
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.
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How To Implement Partial Indexes in ClickHouse
ChistaDATA Cloud

How to Implement Partial Indexes in ClickHouse

Shiv Iyer
Introduction Partial indexes are a powerful feature in ClickHouse that allow DBAs to index only a subset of the rows in a table based on a specified condition. This can significantly reduce the index size […]
How to Implement Reserved Connections in ClickHouse
ClickHouse Performance

How to Implement Reserved Connections in ClickHouse

Shiv Iyer
Reserved connections in ClickHouse can be used to improve query performance by reserving a portion of the available connections for specific users or use cases. By reserving connections, you can ensure that high-priority queries have […]
Implementing Inverted Indexes in ClickHouse for Fast Search: Part 2
Inverted Index

Implementing Inverted Indexes in ClickHouse for Fast Search (Part 2)

Shiv Iyer
Introduction Inverted indexes are a common technique used in search engines and database systems to quickly search for and retrieve data. In ClickHouse, inverted indexes are implemented using a combination of algorithms and data structures. […]
Comparing ClickHouse v/s Hadoop for Real-time Analytics Capability
Comparative Hadoop

Comparing ClickHouse v/s Hadoop for Real-time Analytics Capability

Shiv Iyer
Introduction Hadoop and ClickHouse are two different systems that serve different purposes when it comes to processing and analyzing data. Hadoop is a distributed computing platform that is designed to process and analyze large volumes […]
Leveraging ClickHouse to Build Real-time Credit Card Fraud Detection in Modern Banking
Banking

Leveraging ClickHouse to Build Real-time Credit Card Fraud Detection in Modern Banking

Shiv Iyer
Introduction Credit card fraud analytics systems have migrated from traditional OLAP to ClickHouse based real-time analytics systems because traditional OLAP systems have limitations in processing and analyzing large volumes of data in real-time. Limitations of […]
Data Science in ClickHouse: Normal Distributions and t Distributions
Data Science

Data Science in ClickHouse: Normal Distributions and t Distributions

Shiv Iyer
Introduction The normal distribution and t-distribution are two of the most commonly used probability distributions in statistics. They are used to model the distribution of continuous variables, such as heights, weights, and test scores, and […]
No Picture
ChistaDATA

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 […]
How to Implement the Black-Scholes Model in ClickHouse?
Data Science

How do Data Scientists use Black-Scholes model on ClickHouse?

Shiv Iyer
Introduction The Black-Scholes model is a mathematical formula used to estimate the price of European-style options, which are financial contracts that give the holder the right, but not the obligation, to buy or sell an […]
ClickHouse Troubleshooting: How to Monitor I/O Subsystem Reads
Troubleshooting IO

ClickHouse Troubleshooting: How to Monitor I/O Subsystem Reads

Shiv Iyer
Introduction If the I/O subsystem reads in ClickHouse are struggling, it can lead to slower query performance and longer query execution times. Here are a few ways to tell if the I/O subsystem reads in […]
Use cases for Real-time Analytics with ClickHouse in Modern Banking
ClickHouse

Use cases for Real-time Fraud Analytics with ClickHouse in Modern Banking

Shiv Iyer
Introduction The modern banking industry faces significant challenges related to fraud prevention. Fraudsters are becoming increasingly sophisticated, and traditional fraud detection methods are often insufficient to detect and prevent fraudulent activities. Real-time fraud analytics is […]

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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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