We are a full-stack ClickHouse infrastructure operations Consulting(ClickHouse Consulting), Consultative Support(24*7) and Managed Services provider with core expertize in performance, scalability and data SRE. ChistaDATA stands as the industry’s leading full-stack ClickHouse infrastructure operations provider, delivering enterprise-grade consultative support and managed services for organizations demanding sub-second analytics at petabyte scale.

Our expertise encompasses the full spectrum of ClickHouse’s revolutionary analytics capabilities, including OLAP processing, real-time analytics, and data warehousing solutions that deliver insights within milliseconds. ClickHouse’s column-oriented architecture provides 100x faster processing for analytical queries compared to traditional databases, making it the optimal choice for high-performance analytics. You can download ChistaDATA corporate profile here

To learn how ChistaDATA successfully built Real-Time Analytics and Machine Learning Infrastructure for planet-scale Database Infrastructure, Please download ChistaDATA Full-Stack Database Infrastructure Engineering and Operations Flyer for AI/ML/Real-Time Analytics here

Unlock the Power of Real-Time Analytics with ClickHouse: The Ultimate OLAP Database Solution

Discover why ClickHouse is revolutionizing data analytics for businesses worldwide – from startups to Fortune 500 companies

In the era of big data, organizations are generating unprecedented volumes of information every second. The challenge isn’t just storing this data—it’s extracting meaningful insights fast enough to drive business decisions. Enter ClickHouse, the game-changing database that’s transforming how companies approach analytics.

What is ClickHouse? The Analytics Powerhouse Explained

ClickHouse is a high-performance, column-oriented SQL database management system (DBMS) designed specifically for Online Analytical Processing (OLAP). Unlike traditional databases that struggle with massive datasets, ClickHouse was built from the ground up to handle the most demanding analytical workloads with unprecedented speed and efficiency.

Why ClickHouse Outperforms Traditional Databases

Lightning-Fast Column-Oriented Architecture

The secret to ClickHouse’s exceptional performance lies in its column-oriented storage architecture. This innovative approach delivers several game-changing advantages:

Faster Data Retrieval: Only reads necessary columns from disk, dramatically reducing I/O operations
Superior Aggregation Performance: Optimized for the analytical queries that matter most to your business
Exceptional Compression: Column-based storage enables better compression ratios, reducing storage costs

Unmatched Performance Metrics

ClickHouse doesn’t just promise fast analytics—it delivers measurable results:

Process over a billion rows per second
Handle billions or trillions of rows with ease
Deliver results in near real-time for complex analytical queries

Real-World Applications: Where ClickHouse Excels

Business Intelligence and Reporting

Transform your reporting capabilities with sub-second query responses across massive datasets. Whether you’re analyzing customer behavior, financial performance, or operational metrics, ClickHouse ensures your dashboards update in real-time.

Real-Time Analytics

Monitor your business as it happens. From fraud detection to personalized recommendations, ClickHouse enables immediate insights that drive competitive advantage.

Data Science and Machine Learning

Accelerate your data science workflows with rapid feature engineering and model training data preparation across petabyte-scale datasets.

IoT and Time-Series Analytics

Process millions of sensor readings and events per second, making ClickHouse ideal for IoT applications, monitoring systems, and time-series analysis.

Technical Architecture and Core Differentiators

Columnar Storage Engine with MergeTree Architecture

ClickHouse implements a columnar OLAP database management system built on the MergeTree storage engine family. Unlike traditional row-oriented databases that store complete records together, ClickHouse stores each column as independent files on disk. This architecture enables:

Vectorized query execution that fits more values into processor registers per operation, directly increasing throughput
Superior compression ratios through column-specific compression algorithms, reducing I/O overhead and improving query performance
Granular data access where the primary key references blocks of 8,192 rows (granules) rather than individual rows

Advanced SQL Processing with Intelligent Query Optimization

ClickHouse provides comprehensive SQL support through sophisticated query optimization mechanisms:

Adaptive Join Algorithm Selection

The system automatically selects optimal join strategies based on data characteristics:

Hash joins for memory-bound operations with smaller datasets
Grace hash joins for datasets exceeding available memory
Merge joins (full sorting and partial merge variants) to minimize memory consumption
Direct joins leveraging key-value storage engines (Join, EmbeddedRocksDB, Dictionary)

Probabilistic Approximation Functions

ClickHouse implements approximate calculations delivering 10-100x performance improvements:

HyperLogLog algorithm (uniqHLL12) using 2^12 5-bit cells (~2.5KB state size) for cardinality estimation with ~10% error margin
Fixed memory consumption (~12KB) for approximate COUNT DISTINCT operations versus O(n) memory requirements for exact calculations
Probabilistic data structures enabling real-time analytics on massive datasets

Sparse Primary Indexing System

ClickHouse employs a unique sparse primary index architecture:

Granule-based indexing storing the first value from every 8,192nd row
Binary search optimization over index marks for compound key queries
Physical data sorting on disk according to ORDER BY clause, enabling efficient range queries
Compressed index storage with optional primary key compression

Scalable Deployment Architecture

ClickHouse supports multiple deployment topologies:

On-Premises Infrastructure

Single-server deployments for development and small-scale analytics
Multi-node clusters with horizontal sharding and replication capabilities
Complete administrative control over hardware resources and configuration parameters

Cloud-Native Architecture

Managed service offerings with automatic scaling and maintenance
Elastic resource allocation based on workload demands
Built-in high availability with automated failover mechanisms

Hybrid Cloud Integration

Cross-environment data synchronization between on-premises and cloud instances
Workload distribution optimizing for cost and performance requirements
Unified query interface across distributed infrastructure components

Performance Engineering and Resource Optimization

Compression and I/O Efficiency

ClickHouse achieves industry-leading compression ratios through:

Column-specific compression algorithms optimized for data type characteristics
Reduced disk I/O operations through efficient data encoding
CPU-I/O trade-off optimization where compression overhead is outweighed by I/O reduction

Query Execution Performance

Technical performance characteristics include:

10x to 100x performance improvements over traditional row-oriented databases through columnar processing
Vectorized execution engine maximizing CPU instruction throughput
Parallel query processing across multiple CPU cores and cluster nodes
Memory-efficient algorithms reducing resource consumption during complex analytical operations

Implementation Architecture and Technical Services

Infrastructure Assessment and Optimization

Technical evaluation encompasses:

Current system architecture analysis identifying bottlenecks and optimization opportunities
Data model design optimizing table schemas for specific query patterns
Primary key selection strategies balancing query performance and storage efficiency

Migration and Integration Engineering

Technical implementation includes:

Zero-downtime migration strategies preserving data integrity during transitions
ETL pipeline optimization for high-throughput data ingestion
Query performance tuning through index optimization and execution plan analysis

Operational Excellence and Performance Monitoring

Ongoing technical support covers:

Query execution profiling identifying performance bottlenecks
Resource utilization optimization through configuration tuning
Cluster scaling strategies for growing analytical workloads
Advanced feature implementation including machine learning pipeline integration

Technical Competitive Advantages

ClickHouse’s architecture delivers measurable technical benefits:

Sub-second query response times on trillion-row datasets
Linear scalability through horizontal partitioning and distributed query execution
Cost-effective resource utilization through efficient compression and vectorized processing
Simplified operational complexity reducing infrastructure management overhead

The columnar architecture, combined with adaptive query optimization and probabilistic approximation capabilities, positions ClickHouse as a purpose-built solution for modern analytical workloads requiring both scale and performance.

Unleashing Real-Time Insights: Why CIOs worldwide choose ClickHouse for Advanced Analytics?

Vectorized Query Execution Engine: ClickHouse implements a true columnar DBMS architecture with vectorized query execution, where operations are dispatched on arrays (vectors or chunks of columns) rather than individual values. The MergeTree storage engine provides columnar storage with custom partitioning, sparse primary indexes, and background data merge operations. This architecture enables sub-millisecond query latency through SIMD (Single Instruction, Multiple Data) processing capabilities and parallel execution across multiple processing lanes.

Distributed Computing Architecture with Horizontal Scalability: The platform utilizes a distributed architecture supporting both vertical scaling (increased cores per node) and horizontal scaling (additional compute nodes). ClickHouse Keeper provides coordination services for data replication, serving as an optimized replacement for ZooKeeper with improved resource utilization and reliability guarantees. The system automatically handles shard distribution and replica management across cluster nodes without performance degradation.

Advanced Compression and Storage Optimization: ClickHouse employs sophisticated compression algorithms including ZSTD (default compression level 1) and LZ4 for optimal storage efficiency. The ZSTD algorithm provides parallelized compression/decompression with consistent performance (approximately 20% variance) while achieving superior compression ratios. The columnar storage format combined with specialized codecs enables compression ratios of 10:1 or higher depending on data characteristics.

Multi-Modal Data Ingestion Pipeline: The platform supports real-time streaming ingestion, batch processing, and asynchronous replication through the MergeTree engine family. Data parts are organized as immutable table segments with background merge operations that consolidate smaller parts into larger, optimized structures. This architecture enables continuous data ingestion while maintaining query performance through efficient merge scheduling algorithms.

Sparse Primary Index Architecture: ClickHouse implements sparse primary indexes that index granules (default 8,192 rows per granule) rather than individual rows. This design reduces index size by orders of magnitude compared to traditional B-tree indexes while enabling efficient range scans and data skipping. The primary index contains one entry per granule, specifically indexing the first row of each granule for optimal memory utilization.

Real-Time Analytics with Sub-Second Latency: The vectorized execution engine processes queries at rates exceeding 2.29 billion rows per second with throughput of 27.56 GB/s. Query processing utilizes parallel execution lanes controlled by max_threads settings, enabling optimal CPU core utilization. The system supports complex analytical operations including aggregations, joins, and window functions with microsecond-level response times.

Enterprise-Grade Fault Tolerance: High availability is achieved through ReplicatedMergeTree table engines with automatic failover capabilities. ClickHouse Keeper clusters (minimum three nodes recommended) provide distributed consensus for replica coordination and metadata management. The system includes built-in checksums, data integrity verification, and automatic recovery mechanisms for hardware failures.

Comprehensive Security Framework: The platform implements Role-Based Access Control (RBAC) with granular permissions at database, table, row, and column levels. Authentication mechanisms include SSL certificate-based authentication, LDAP integration, and custom external authenticators. Data encryption supports both at-rest and in-transit protection with configurable cipher suites and key management.

API Integration and Protocol Support: ClickHouse provides native TCP protocol, HTTP/HTTPS interfaces, and JDBC/ODBC connectivity. The system supports multiple data formats including Parquet, ORC, JSON, CSV, and native ClickHouse formats for seamless integration with existing data ecosystems. Protocol-level compression with LZ4 and ZSTD reduces network overhead for distributed deployments.

Open-Source Ecosystem and Community Infrastructure: The platform benefits from active open-source development with comprehensive documentation, performance benchmarking tools (clickhouse-benchmark), and community-contributed connectors. The ecosystem includes specialized tools for query optimization, performance monitoring, and cluster management, supported by extensive technical resources and professional collaboration networks.

☛ ColumnStore and Row-Based Database Managed System – Why it’s better to use ColumnStores for SORT/SEARCH intensive Analytics Operations

☛ Why is ClickHouse recommended for a time-series Database?

ClickHouse is a column-oriented, distributed relational database management system that is designed for OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) workloads. It is particularly well-suited for time-series data analysis because of its ability to handle large amounts of data, high write and read performance, and support for advanced analytical functions. Here are some of the reasons why ClickHouse is recommended for time-series data:

Column-oriented storage: ClickHouse uses a column-oriented storage model, which means that data is stored by columns rather than by rows. This allows for efficient compression and faster data retrieval, especially for time-series data, where the data is often read in time-based chunks.
Advanced analytical functions: ClickHouse supports advanced analytical functions such as window functions, aggregate functions, and SQL-based data filtering, which are useful for time-series data analysis. This allows users to perform complex queries on large data sets quickly and efficiently.
Real-time query performance: ClickHouse is designed to handle high write and read performance, making it suitable for real-time data analysis. It can handle millions of writes per second and return results in milliseconds, even on large datasets.
Scalability: ClickHouse is a distributed system, which means that it can scale horizontally by adding more servers. This allows it to handle very large data sets and handle high write and read loads.
Compression: ClickHouse supports advanced compression techniques, which can significantly reduce the size of the data stored on disk, making it more cost-efficient for storing large data sets.
High Availability: ClickHouse supports high availability through replication. It allows data to be replicated across multiple servers, which can help to ensure that data is always available even in the event of a server failure

☛ Why do we recommend ClickHouse over many other columnar database systems?

Optimized Columnar Storage Architecture – ClickHouse implements a true column-oriented storage system where ten billion UInt8 values consume exactly 10GB of uncompressed memory (1 byte per value), ensuring optimal CPU cache utilization and memory bandwidth efficiency. The columnar layout eliminates storage overhead and garbage collection, providing deterministic memory footprint calculations for capacity planning.

Vectorized Query Execution Engine – ClickHouse employs a state-of-the-art vectorized query processing layer that dispatches operations on arrays (vectors or column chunks) rather than individual scalar values. This architecture leverages SIMD (Single-Instruction-Multiple-Data) CPU registers to process multiple values simultaneously in a single instruction cycle, significantly reducing per-row processing overhead and maximizing CPU throughput.

Advanced Compression Algorithms – ClickHouse supports two primary compression codecs with distinct performance characteristics:

LZ4: Optimized for decompression speed (~500 MB/s encoding), recommended when I/O bandwidth exceeds decompression capacity
ZSTD: Superior compression ratio (2.8:1 typical) with parallelizable decompression (~20% variance), ideal for I/O-bound workloads with large range scans
Adaptive compression: “none” option available for ultra-fast NVMe storage subsystems where compression overhead exceeds I/O benefits⁷

Persistent Storage Capability – Unlike in-memory columnar systems (SAP HANA, Google PowerDrill), ClickHouse implements persistent disk-based storage with intelligent caching strategies, enabling datasets exceeding available RAM while maintaining sub-second query performance.

Massively Parallel Processing (MPP) Architecture – ClickHouse utilizes a shared-nothing MPP architecture that distributes query execution across multiple nodes. The system parallelizes query plan operators and passes intermediate results in batches rather than single rows, maximizing resource utilization across CPU cores and cluster nodes.

Distributed Processing and Sharding – ClickHouse supports horizontal scaling through distributed tables and automatic sharding mechanisms. Each shard operates as an independent processing unit with optional replica groups, providing linear scalability and fault tolerance through redundant data placement.

Sparse Primary Index with MergeTree Engine – ClickHouse implements a sparse primary index system within the MergeTree storage engine. Data is incrementally sorted using background merge operations, enabling efficient range queries on primary key values without row-level locking during concurrent inserts. The sparse index significantly reduces memory overhead while maintaining query performance through data skipping.

Statistical Aggregation and Sampling – ClickHouse provides specialized aggregate functions for approximate calculations including HyperLogLog for cardinality estimation, quantile approximation algorithms, and sampling-based aggregation. The system supports partial aggregation on random key subsets, enabling statistical analysis on data samples with configurable accuracy-performance trade-offs.

SQL Compliance and Query Capabilities – ClickHouse implements ANSI SQL with support for complex subqueries in FROM, IN, and JOIN clauses, plus scalar subqueries. The system excludes dependent (correlated) subqueries to maintain query optimization predictability and performance characteristics.

Asynchronous Multi-Master Replication – ClickHouse employs an asynchronous, multi-master replication architecture where INSERT and ALTER operations can target any available replica. Data propagation occurs in the background with eventual consistency guarantees, and the system provides automatic recovery mechanisms for most failure scenarios while maintaining identical data across replica sets.

☛ Building advanced Data Science, Machine Learning and AI with ChistaDATA Real-Time Analytics Infrastructure

Real-time analytics significantly influences the field of Data Science and AI by enabling faster and more actionable insights from data. Here are a few ways real-time analytics is impacting Data Science and AI:

Timely Decision-Making: Real-time analytics allows organizations to make decisions and take actions based on up-to-date information. Data scientists and AI algorithms can analyze data as it arrives in real-time, providing immediate insights that can drive business decisions, optimize processes, and respond quickly to emerging trends or events.
Dynamic Model Training: Real-time analytics enables data scientists to continuously update and refine their models using fresh data. Instead of relying on static, batch processing approaches, real-time data streams can be fed into machine learning algorithms, allowing models to adapt and learn in real-time. This improves the accuracy and relevance of AI predictions and recommendations.
Rapid Detection of Anomalies and Fraud: Real-time analytics helps detect anomalies and fraud in real-time data streams. Machine learning algorithms can continuously monitor data patterns, identify deviations, and trigger alerts or take automated actions to mitigate risks. This is particularly valuable in industries such as finance, cybersecurity, and e-commerce, where quick detection and response to anomalies are critical.
Personalization and Customer Experience: Real-time analytics enables personalized experiences and recommendations for users in real-time. By analyzing user behavior, preferences, and contextual data in real-time, AI algorithms can deliver tailored content, product recommendations, and personalized marketing messages. This enhances customer satisfaction and engagement.
Predictive Maintenance and IoT: Real-time analytics is vital in predictive maintenance for IoT devices and systems. By monitoring real-time sensor data and applying AI algorithms, organizations can predict equipment failures, detect anomalies, and proactively schedule maintenance. This helps optimize maintenance schedules, reduce downtime, and improve operational efficiency.
Streaming Data Analysis: Real-time analytics allows organizations to process and analyze massive data streams in motion, such as social media feeds, sensor data, and transaction logs. Data scientists can leverage streaming data processing frameworks and AI algorithms to extract insights and derive valuable information in real time, enabling immediate actions and responses.
Fraud Detection and Cybersecurity: Real-time analytics is instrumental in identifying and preventing fraudulent activities and enhancing cybersecurity. By continuously monitoring data streams, AI algorithms can quickly detect suspicious patterns, unauthorized access attempts, and potential security breaches. The real-time analysis enables immediate responses, such as blocking suspicious transactions or triggering security alerts, ensuring the protection of sensitive data and systems.
Operational Efficiency and Resource Optimization: Real-time analytics helps optimize operational processes and resource allocation. By analyzing real-time data from various sources, organizations can identify bottlenecks, streamline workflows, and allocate resources more efficiently. Data-driven insights enable proactive decision-making, such as adjusting production schedules, optimizing supply chain logistics, or managing workforce allocation, improving efficiency and cost savings.
Risk Management and Compliance: Real-time analytics is crucial for effective risk management and regulatory compliance. Organizations can continuously monitor data and apply AI algorithms to identify and assess potential risks in real-time, enabling proactive risk mitigation strategies. Real-time analytics also helps ensure compliance with industry regulations and standards by monitoring data for any anomalies or violations and taking immediate corrective actions.
Real-time Data Visualization and Dashboards: Real-time analytics enables the creation of dynamic, interactive visualizations and dashboards that provide real-time insights to stakeholders. Data scientists and AI practitioners can leverage visualization tools and techniques to present complex data in a digestible format, allowing users to monitor key metrics, track performance, and make informed decisions on the fly.

☛ Why do successful companies work with ChistaDATA 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.

☛ Building high-Performance MySQL, MariaDB, and PostgreSQL Transaction Processing Systems with ChistaDATA Real-Time Data Archiving Toolkit

In today’s data-driven world, organizations often face challenges related to the performance and scalability of their traditional relational databases like PostgreSQL, MySQL, and MariaDB. To overcome these limitations and unlock the full potential of their data, many businesses are turning to ClickHouse, a high-performance columnar database. One practical approach is to archive historical data from PostgreSQL, MySQL, and MariaDB to ClickHouse. This allows organizations to retain their valuable data for long-term storage and analysis while benefiting from the superior performance and scalability of ClickHouse. Let’s explore the benefits and the process of archiving data to ClickHouse.

Benefits of Archiving Data to ClickHouse:

Improved Performance: ClickHouse’s columnar storage format and optimized query execution engine provide significant performance improvements for analytical workloads. By archiving historical data to ClickHouse, organizations can offload the data from their traditional databases, reducing the query load and enhancing performance for active transactional systems.
Cost-Effective Storage: ClickHouse’s efficient compression algorithms and storage optimizations enable organizations to store large volumes of data cost-effectively. By moving historical data to ClickHouse, organizations can reduce the storage costs associated with their primary databases while retaining easy access to the archived data for analysis and reporting.
Scalability and Capacity: ClickHouse’s distributed architecture and horizontal scalability allow organizations to handle massive amounts of data with ease. Archiving data to ClickHouse ensures that the database infrastructure can scale seamlessly as data volumes grow, providing organizations with the flexibility to accommodate future data growth.
Simplified Data Management: By centralizing historical data in ClickHouse, organizations can simplify their data management processes. ClickHouse’s powerful data ingestion capabilities, data replication features, and SQL-based querying enable efficient data handling and analysis without the complexities often associated with traditional databases.

Process of Archiving Data to ClickHouse:

Data Selection: Identify the data in PostgreSQL, MySQL, or MariaDB that needs to be archived. This typically includes historical or less frequently accessed data that is no longer actively used in transactional operations.
Data Extraction: Extract the selected data from the source database. This can be done using various methods, such as SQL queries or ETL processes, depending on the database technology and the specific data extraction requirements.
Data Transformation and Formatting: Convert the extracted data into a format suitable for ClickHouse. This may involve transforming the data schema, adjusting data types, and ensuring compatibility with ClickHouse’s columnar storage format.
Data Loading into ClickHouse: Utilize ClickHouse’s native data ingestion mechanisms, such as the ClickHouse SQL interface, ClickHouse client libraries, or external data integration tools, to load the archived data into ClickHouse tables. ClickHouse’s high-speed data loading capabilities ensure efficient and fast data ingestion.
Indexing and Query Optimization: Create appropriate indexes on the archived data in ClickHouse to optimize query performance. Analyze the query patterns and design indexes that align with the specific analytical requirements of the archived data.
Data Retention and Archiving Strategy: Define a data retention policy and archiving strategy based on the organization’s specific needs. This includes determining the duration of data retention in ClickHouse and establishing periodic archiving processes to ensure efficient archived data management.
Data Access and Analytics: Leverage ClickHouse’s powerful SQL capabilities, analytical functions, and data manipulation tools to perform advanced analytics on archived data. ClickHouse’s real-time query processing capabilities enable organizations to gain valuable insights from historical data for decision-making and business intelligence purposes.

☛ ClickHouse Consulting Plans (we do both on-site and remote ClickHouse consulting) from ChistaDATA Inc.

We are available on short notice if you are building a web-scale columnar database systems analytics and your business demands on-site ClickHouse consultants. We work very closely with your team on-site,, guiding them strategically and technically on building optimal, scalable and highly available ClickHouse database infrastructure operations.

On-Site ClickHouse Consulting from ChistaDATA Inc.	Rate ( plus GST / Goods and Services Tax where relevant )
Per-Diem	US $600 / hour

We can do almost everything remote on ClickHouse, This includes performance, scalability and high availability. Our technical account manager will be working very closely with your team to understand the goals and build short/long-term deliverables managing ChistaDATA ClickHouse Consultants.

Remote ClickHouse Consulting by ChistaDATA Inc.	Rate ( plus GST / Goods and Services Tax where relevant )
Per Diem	US $450 / hour

If you are a startup, We have flexible ClickHouse Managed Services options available:

Avg. Hours / Month	Quarterly ( plus GST / Goods and Services Tax where relevant )	Six-Monthly ( plus GST / Goods and Services Tax where relevant )	Annually ( plus GST / Goods and Services Tax where relevant )
4	US $7,500.00	US $10,500.00	US $25,500.00
8	US $10,800.00	US $15,500.00	US $30,500.00
12	US $12,800.00	US $18,500.00	US $35,500.00
16	US $15,500.00	US $22,500.00	US $40,000.00
20	US $18,500.00	US $26,500.00	US $50,500.00
24	US $23,000.00	US $30,000.00	US $55,500.00
28	US $28,500.00	US $36,500.00	US $62,000.00
32	US $33,500.00	US $42,000.00	US $70,500.00
36	US $40,000.00	US $50,000.00	US $77,000.00
40	US $44,500.00	US $58,500.00	US $85,000.00

☛ ClickHouse Enterprise Support (24*7)

You get access to our seasoned ClickHouse support team 24*7 for an fraction of cost to hiring a full-time Sr. level ClickHouse consultant . We will help you in building an planet-scale data analytics platform using ClickHouse which is optimal, scalable and highly available.

Enterprise-Class ClickHouse Support
- Technical Account Manager to clearly understand your business goals and orchestrate our support operations.
- 30 Minute Response Time on Severity 1 (Urgent) Issues.
- 10 Named Customer Contacts.
- Support channels – Phone, Email, Slack, and Ticketing Systems.
- Technical support — 30 minute response time (S1)
  - Support -levels – We have very well defined support infrastructure operations function:
    - Severity 1– Immediate attention needed, The customer’s business is severely impacted and database infrastructure is unavailable.
      - Response time (SLA) – 30 minutes.
    - Severity 2– Customer database infrastructure is available (up and running) but performance / scalability issues are directly impacting business.
      - Response time (SLA) – 4 hours.
    - Severity 3– Low impact situation, Customer business and production infrastructure is functioning normally, but the problem is impacting the development ecosystems, also causing delay in production deployment.
      - Response time (SLA) – 12 hours.
    - Severity 4– Low to no impact situation, It is more about knowing the features and capability of components before considering the adoption.
      - Response time (SLA) – 48 hours.

ClickHouse DBA Consultative Support
- Recommendations for database architecture and design.
- Recommendations for optimal SQL engineering.
- Recommendations for ClickHouse Performance optimization and tuning.
- Recommendation for index design, optimization and usage.
- Recommendations for ClickHouse backup and disaster recovery.
- Recommendations for ClickHouse high availability and auto failover.
- Recommendations for ClickHouse data archiving and partitioning.
- Recommendations for ClickHouse maintenance operations.

ChistaDATA ClickHouse Enterprise Support	Rate ( plus GST / Goods and Services Tax where relevant )
Unlimited ClickHouse Instances	US $75,000 / Year

☛ How ChistaDATA can help you in building web-scale real-time streaming data analytics using ClickHouse?

Consulting – We are experts in building optimal, scalable (horizontally and vertically), highly available and fault-tolerant ClickHouse powered streaming data analytics platforms for planet-scale internet / mobile properties and the Internet of Things (IoT). Our elite-class consultants work very closely with your business and technology teams to build custom columnar database analytics solutions using ClickHouse.
Database Architect services – We architect, engineer and deploy data analytics platforms for you. We will take care of your data analytics ecosystem so that you can focus on business.
ClickHouse Enterprise Support – We have 24*7 enterprise-class support available for ClickHouse, Our support team will review and deliver guidance for your data analytics platforms architecture, SQL engineering, performance optimization, scalability, high availability and reliability.
ClickHouse Training.
Pay only for hours we have worked for you; This makes us affordable for startups and large corporations equally.

☛ Further Reading

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