Real-time Analytics for Digital Transformation with ChistaDATA’s ClickHouse


Real-time analytics is becoming increasingly popular as a way to gain insights into business operations, customer behavior, and other important metrics. This is due to the growing need for organizations to make data-driven decisions quickly and effectively in order to stay competitive in the digital transformation world. Traditional OLAP (Online Analytical Processing) systems are not able to keep up with the speed and volume of data generated by modern systems, making real-time analytics a more suitable solution.
One of the major advantages of real-time analytics is that it allows organizations to process and analyze data as it is generated, rather than waiting for data to be loaded into a data warehouse or other analytical system. This allows organizations to gain insights into their data much more quickly and make decisions based on the most current information.
One of the key technologies used in real-time analytics is Apache Kafka, which is a distributed streaming platform that is used to handle high volumes of data and transmit it to other systems for analysis. When combined with a high-performance analytical database like ClickHouse, organizations can achieve real-time analytics on large datasets with low latency.
Real-time analytics is increasingly being used in many industries, such as finance, healthcare, retail, and manufacturing, to gain insights into customer behavior, identify trends, and make predictions about future events. As a result, real-time analytics is aggressively replacing traditional OLAP in the digital transformation world.

What happens when you use OLTP Databases like MySQL and PostgreSQL instead of ClickHouse for real-time analytics?

Using OLTP databases like MySQL and PostgreSQL instead of ClickHouse for real-time analytics can have a negative impact on performance. OLTP databases are optimized for transactional workloads and are not typically designed to handle large amounts of data or complex analytical queries. As a result, real-time analytics on OLTP databases can be slow and resource-intensive, leading to poor performance and increased latency. Additionally, OLTP databases typically do not have built-in features for data compression, columnar storage, or vectorized query execution, which are essential for high-performance real-time analytics. Therefore, it is generally recommended to use specialized analytical databases like ClickHouse for real-time analytics to achieve better performance and reliability.

(1) Top 10 reasons why you should not use OLTP Databases like MySQL and PostgreSQL for Analytics

  1. Lack of scalability: OLTP databases like MySQL and PostgreSQL are not designed to handle large amounts of data and high concurrency, which can lead to performance bottlenecks and slow query response times.
  2. Limited analytical capabilities: OLTP databases have limited analytical capabilities and are not optimized for complex queries or data processing.
  3. Poor data compression: OLTP databases often have poor data compression capabilities, which can lead to slow query response times and increased disk space usage.
  4. Limited data modeling options: OLTP databases have limited data modeling options, which can make it difficult to represent complex data structures and relationships.
  5. Inefficient indexing: OLTP databases typically use B-tree indexing, which is not as efficient for analytical queries as columnar storage and other indexing methods used by analytical databases like ClickHouse.
  6. Lack of real-time analytics: OLTP databases are not designed for real-time analytics and may not be able to process and analyze data in near real-time.
  7. Limited data governance: OLTP databases often have limited data governance and security features, which can make it difficult to manage and protect sensitive data.
  8. Limited data visualization options: OLTP databases have limited data visualization options, which can make it difficult to create interactive and meaningful visualizations of data.
  9. Lack of support for distributed computing: OLTP databases may not have built-in support for distributed computing, which can make it difficult to scale horizontally and process large amounts of data.
  10. High operational costs: OLTP databases may have high operational costs due to the need for additional hardware, software, and personnel to manage and maintain them.

(2) How Hadoop solves Big Data Analytics but not recommended for real-time Analytics?

Hadoop is a popular open-source framework for distributed storage and processing of large data sets. It is designed for batch processing of data and can handle large amounts of data stored in distributed clusters. However, it is not recommended for real-time analytics because of the following reasons:
  1. Latency: Hadoop jobs can take a long time to complete, which makes it difficult to get real-time insights from the data.
  2. Complexity: Hadoop requires a lot of setup and configuration before it can be used, which can be complex and time-consuming.
  3. Scalability: Hadoop is not as scalable as other real-time analytics solutions, making it difficult to handle large amounts of data in real-time.
  4. Cost: Hadoop requires a large number of servers and a lot of storage, which can be expensive.
  5. Data Processing: Hadoop requires data to be stored in a specific format, which can be difficult to work with and process.
  6. Query Performance: Hadoop’s query performance is not as good as other real-time analytics solutions, making it difficult to get insights quickly.
  7. Real-time Streaming: Hadoop is not designed to handle real-time streaming data, which is becoming increasingly important in today’s data-driven world.
  8. Limited SQL Support: Hadoop has limited SQL support, making it difficult to perform complex queries and analyses.
  9. Limited Integration: Hadoop doesn’t have good integration with other systems and tools, making it difficult to use in a real-time analytics environment.
  10. Limited Security: Hadoop doesn’t have good built-in security features, making it difficult to ensure the data is protected and secure.

(3) Why is ClickHouse most preferred for real-time analytics?

ClickHouse is most preferred for real-time analytics for several reasons:
  1. Speed: ClickHouse is designed to handle large amounts of data and can perform complex queries on it quickly.
  2. Scalability: ClickHouse can handle a large number of concurrent queries and can be easily scaled up or down to handle changing workloads.
  3. Columnar storage: ClickHouse uses a columnar storage format which is optimized for analytical queries, making it more efficient than row-based storage used in traditional OLTP databases.
  4. Column-level compression: ClickHouse uses column-level compression which reduces the amount of disk space needed to store the data, thus reducing costs.
  5. SQL support: ClickHouse supports a wide range of SQL operations, making it easy to use for data analysts and developers who are familiar with SQL.
  6. Flexibility: ClickHouse can be used for a wide range of use cases, including real-time analytics, OLAP, and data warehousing.
  7. Open source: ClickHouse is open-source software, which means it is free to use and can be easily customized to meet specific requirements.
  8. Robustness: ClickHouse is designed to handle large amounts of data and can handle a large number of concurrent queries, making it a robust choice for real-time analytics.
  9. Fault-tolerance: ClickHouse provides fault-tolerance by replication of data, which means that data is automatically replicated across different servers, ensuring that data is not lost even in case of server failures.
  10. Integration: ClickHouse can be easily integrated with other systems, including Apache Kafka, which allows it to be used in real-time analytics pipelines.

(4) How can you use ClickHouse with OLTP Databases like MySQL and PostgreSQL for performance and reliability?

ClickHouse can be used in conjunction with OLTP databases like MySQL and PostgreSQL to achieve both performance and reliability. One approach is to use ClickHouse as a real-time OLAP system to perform high-speed analytics on the data stored in the OLTP databases. This can be done by replicating data from the OLTP databases to ClickHouse in real-time using a tool such as the ClickHouse Kafka engine or a custom Python script. Once the data is in ClickHouse, advanced analytics can be performed using SQL queries, while the OLTP databases continue to handle the transactional workload. Another approach is to use ClickHouse as a data warehousing solution and use the OLTP databases as a source of data which is periodically extracted, transformed and loaded into ClickHouse. This allows you to offload the analytical workload from the OLTP databases while retaining transactional consistency. Additionally, ClickHouse’s ability to handle high write and read concurrency, and its columnar storage format can help improve performance and scalability.

(5) How real-time Analytics is deployed with Apache Kafka and ClickHouse?

Real-time analytics can be deployed with Apache Kafka and ClickHouse by utilizing Kafka’s ability to handle streaming data and ClickHouse’s ability to perform real-time analytics. The data is ingested into Kafka and then consumed by ClickHouse. ClickHouse can then process the data in real-time, allowing for near-instant insights and analysis. This can be done by using a Kafka Connector for ClickHouse which allows ClickHouse to consume data from Kafka, this connector can be implemented using the Kafka API, or using a Kafka Connect sink connector. Once the data is in ClickHouse, it can be queried, visualized and analyzed using SQL queries or with the help of any BI tool which can connect to ClickHouse. This setup enables data to be analyzed as it is being generated, allowing for real-time insights and decision-making.

(6) Why do successful companies work with ChistDATA for ClickHouse Consultative Support and Managed Services?

  • ChistaDATA provides full-stack ClickHouse Optimization. We deliver elite-class Consultative Support (24*7) and Managed Services for both on-premises ClickHouse infrastructure and Serverless/Cloud/ClickHouse DBaaS operations.
  • ChistaDATA Server for ClickHouse (and all tools essential for Data Ops. @ Scale) will be Open Source (100% GPL forever) and free. We are committed to helping corporations in building Open Source ColumnStore for high-performance Data Analytics.
  • Global Team available 24*7 for ClickHouse Consultative Support and Managed Services.
  • Our team has built and managed Data Ops. Infrastructure of some of the largest internet properties. We know very well the best practices for building optimal, scalable, highly reliable and secured Database Infrastructure @ scale.
  • Lean Team Culture: Startup-friendly and specialists in DevOps. and Automation for Database Systems Maintenance Operations.
  • Transparent pricing and no hidden charges – We have both fixed-priced and flexible subscription plans.
  • Based out of San Francisco Bay Area. But, we have global teams operating from 11 cities worldwide to deliver 24*7 Consultative Support and Managed Services for ClickHouse.

To read more real-time analytics in ClickHouse, do consider reading the below articles

About Shiv Iyer 215 Articles
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.