Introduction Hadoop is a powerful, open-source ecosystem for storing and processing large datasets, but it is not well-suited for real-time analytics. Hadoop’s limitations in Real-time Analytics Here are a few reasons why Hadoop is not […]
Introduction ClickHouse uses a thread pool to handle and manage threads. The thread pool is responsible for handling incoming client connections, executing SQL queries, and performing other tasks. Each client connection is handled by a […]
Introduction A financial technology (FinTech) company may integrate Kafka with ClickHouse to enable real-time stream processing of financial data, across a variety of use cases such as merchant settlements, fraud analytics, etc. Runbook to integrate […]
Introduction ClickHouse is a column-oriented, open-source analytics database that is designed for real-time OLAP (online analytical processing) and OLTP (online transaction processing) use cases. It is optimized for large-scale data processing, high-performance queries, and real-time […]
Introduction Setting up real-time streaming bulk data loading from Kafka to ClickHouse involves several steps: Install and configure Kafka: Install and configure Apache Kafka on a cluster of machines. This will allow you to produce […]
Introduction ClickHouse is a column-oriented database management system that is designed for real-time analytics, and it has several features that make it well-suited for this type of workload, compared to PostgreSQL Comparing ClickHouse with PostgreSQL […]
Introduction ClickHouse uses a memory management system to control the allocation and deallocation of memory resources. The main components of the memory management system are: Memory Pool: ClickHouse uses a memory pool to manage the […]
Introduction ClickHouse supports several storage engines, each with its own characteristics and performance characteristics. The storage engine you choose will depend on the specific requirements of your use case and the data you are working […]
Introduction Tuning ClickHouse configuration parameters for performance can be a complex task, but there are a few key parameters that can have a significant impact on performance. Here is a general guide for tuning ClickHouse […]
Introduction ClickHouse uses a SQL parser to process SQL statements and convert them into a query execution plan. The parser is responsible for validating the syntactic and semantic correctness of SQL statements, and for transforming […]
PostgreSQL is a registered trademark of the PostgreSQL Community Association. ClickHouse is a registered trademark of ClickHouse, Inc. MongoDB is a registered trademark of MongoDB, Inc. Couchbase is a registered trademark of Couchbase, Inc. Redis is a registered trademark of Redis Ltd. Apache Cassandra is a registered trademark of the Apache Software Foundation. Milvus is a registered trademark of Zilliz. MinIO is a registered trademark of MinIO, Inc. Amazon Redshift and Amazon Aurora are registered trademarks of Amazon.com, Inc. Google Cloud is a registered trademark of Google LLC. Snowflake is a registered trademark of Snowflake Inc. Databricks is a registered trademark of Databricks, Inc. MySQL and InnoDB are registered trademarks of Oracle Corporation. Oracle is a registered trademark of Oracle Corporation. MariaDB is a trademark of MariaDB Corporation Ab. All other trademarks are property of their respective owners. Other product or company names mentioned may be trademarks or trade names of their respective owner. Copyrights © 2010-2025. All Rights Reserved by ChistaDATA®.