February 2026 Summaries

Tinybird has enhanced its branching feature to facilitate agentic development by fully supporting connectors like Kafka, S3, and GCS, allowing for isolated environments where agents can develop, test, and iterate without affecting production data. These branches enable developers to create sandboxes, where they can define data sources and endpoints, ingest real data through connectors, test results, and make iterations. With the use of the --with-connections flag, connectors are automatically created within a branch, and branches inherit production secrets by default, but they can be overridden to point to staging or development resources. Kafka connections in branches create new consumer groups and start consuming data from the current offset, while S3 and GCS connectors support sample imports for realistic data testing. Preview deployments from CI allow for ephemeral branches per pull request, ensuring isolated environments for testing changes, which can be automatically managed by the SDK in various CI environments. This system not only provides a safe space for development but also integrates seamlessly with existing tools, offering a streamlined and efficient workflow for developers.

ClickHouse® integration with MySQL offers three main pipeline options for transferring data from the widely-used OLTP database MySQL to the columnar OLAP database ClickHouse® for enhanced analytical queries and real-time dashboards. These options include using Tinybird, which facilitates managed MySQL CDC ingestion and API publishing, ClickHouse® Cloud's ClickPipes featuring a native MySQL CDC connector, or a self-managed setup using tools like Debezium and Kafka for complete control over the replication process. Each method utilizes MySQL's binary log (binlog) to capture and apply database changes, with the choice between them depending on factors like operational preferences, control needs, API requirements, and the ability to handle replication layers. Tinybird offers a streamlined solution with minimal operational overhead and built-in API capabilities, whereas ClickHouse® Cloud and self-managed options allow for more customization and control. These pipelines ensure that MySQL remains optimized for transaction processing while ClickHouse® handles the heavy lifting of analytical queries, thereby maintaining the efficiency and performance of the primary operational database.

Tinybird is discontinuing its AI-powered CLI, Tinybird Code, which was designed to facilitate the development of Tinybird projects using natural language prompts and deep ClickHouse knowledge. Despite its effectiveness, the decision to sunset Tinybird Code stems from a broader realization about the evolution of coding agents, which have rapidly matured and no longer require custom wrappers to function effectively. The development process for Tinybird Code significantly improved the platform by enhancing error messaging, streamlining the CLI, and refining documentation, making it more agent-friendly. Instead of maintaining a custom agent, Tinybird has distilled its domain knowledge into portable Agent Skills that can be utilized by any coding agent, allowing for a seamless integration with existing workflows and continuous improvement as agents evolve. This shift prioritizes creating a platform compatible with all agents, emphasizing clear interfaces, predictable behavior, and useful feedback, while encouraging users to adopt the skills approach for a more adaptable and robust experience.

Integrating NATS with ClickHouse® for real-time data analytics can be achieved through three main methods: using Tinybird, self-managed ClickHouse®, or a custom consumer or ETL service. NATS is a lightweight messaging system that, when paired with ClickHouse®, allows for low-latency messaging and high-volume analytical queries. Tinybird offers a managed solution where applications consume NATS messages and send them to Tinybird’s Events API, which then handles storage and scaling in ClickHouse®, providing easy API publishing without needing to manage the NATS engine. In contrast, self-managed ClickHouse® uses the NATS table engine directly, allowing for native integration but requiring operational management and tuning. Alternatively, a custom consumer can be designed to read from NATS and write to ClickHouse® or Tinybird, offering flexibility for custom routing and transformation needs. The choice among these options depends on operational capacity, the need for APIs, and whether a managed or self-managed setup is preferred.

ClickHouse® integration with NATS allows for seamless real-time analytics and reporting by connecting NATS, a lightweight messaging system, to ClickHouse®, a columnar OLAP database optimized for high-volume event and log data. Three primary methods are outlined for implementing this integration: using Tinybird, which offers a managed solution with minimal operational requirements and built-in API publishing; a self-managed ClickHouse® setup using the NATS table engine, which requires operational expertise but provides full control; and a custom consumer or ETL service, which allows for custom transformations and routing. This integration is ideal for scenarios requiring analytical queries over large data volumes, such as application event analytics, operational metrics, and real-time dashboards, offering flexibility based on the operational preferences and analytical needs of the user.

The text explores various options for integrating PostgreSQL with ClickHouse® for real-time analytics and reporting, focusing on three main approaches: using Tinybird, ClickHouse® Cloud ClickPipes, and a self-managed setup with Debezium and Kafka. Tinybird offers a streamlined solution with managed Postgres CDC ingestion and built-in API publishing, making it suitable for users seeking minimal infrastructure management. ClickHouse® Cloud provides a native Postgres CDC connector through ClickPipes, allowing for managed replication while requiring users to develop their own API or BI layer. The self-managed option offers the most control, utilizing Debezium and Kafka for change data capture but demands more operational effort. The choice of integration method depends on the level of control desired over the replication process, the need for instant API capabilities, and the operational resources available. Each method ensures PostgreSQL data is captured for analysis in ClickHouse® without burdening the transactional database, optimizing it for large-scale analytics and real-time dashboards.

Connecting RabbitMQ to ClickHouse® is a strategy aimed at leveraging RabbitMQ's message brokering capabilities and ClickHouse®'s strength in handling analytical queries over large data volumes. Three main options exist for establishing this integration: using Tinybird, a managed service that allows applications to consume messages from RabbitMQ and send them to ClickHouse® via the Events API; self-managing ClickHouse® with the RabbitMQ table engine, which directly consumes messages and writes them into a MergeTree table; or implementing a custom consumer or ETL service that can handle specific routing or transformation needs. Each approach offers different advantages depending on operational capacity, the need for APIs, and the level of control desired over the data pipeline. Teams may choose based on their existing infrastructure and whether they prioritize ease of use and managed services or the ability to fine-tune and monitor their own systems.

Clickhouse® integration with Hive addresses the need for querying existing data in Hive, migrating datasets for low-latency analytics, and using Hive Metastore as a catalog for formats like Iceberg or Delta lakehouse. This integration is crucial as it facilitates real-time queries over enterprise data lakes, maintaining compatibility with existing Hadoop infrastructure. Clickhouse® primarily interacts with Hive's catalog and storage layers, not the SQL engine, thus offering interactive analytical performance impossible with traditional Hive queries. Key integration patterns include federated queries, Hive-style partitioning, and lakehouse integration, each solving specific architectural challenges while respecting Hive's batch processing design. The integration leverages Hive for storage, metadata management, and batch processing, while Clickhouse® serves for real-time analytics, emphasizing the importance of proper partitioning and file sizing to minimize metadata overhead. This setup ensures teams can deliver both the durability and governance of Hive and the sub-100ms query latency of Clickhouse®, effectively balancing the strengths of both systems.

The integration of Clickhouse® with Delta Lake addresses architectural choices that enhance real-time querying over transactional lakehouse data while maintaining Delta's ACID guarantees, schema evolution, and time-travel capabilities. This integration is significant as Delta Lake has become the standard lakehouse format on Databricks and other platforms. Key integration patterns include zero-copy queries, catalog-based integration, Change Data Capture (CDC) ingestion, and hybrid architectures, each designed to solve specific latency, consistency, and cost challenges. Teams are encouraged to select integration patterns that align with their operational needs, balancing Delta's batch consistency with Clickhouse®'s interactive query performance. By understanding the transactional integrity and storage efficiencies provided by Delta alongside Clickhouse®'s capabilities for sub-100ms latency and high concurrency, organizations can effectively manage their data architectures. This integration allows for the seamless coexistence of Delta's robust lakehouse foundation and Clickhouse®'s real-time analytical prowess, enabling both governance and performance without compromise.

The integration of Clickhouse® with HDFS addresses distinct challenges for teams using Hadoop data lakes by enabling direct querying of files in HDFS, loading data into MergeTree tables for low-latency serving, continuous ingestion, and utilizing HDFS as a remote storage tier. This integration is significant as enterprise data lakes remain predominantly on HDFS, despite cloud migration trends. The combination leverages HDFS for distributed storage and Clickhouse® for superior analytical query performance, offering solutions such as federated queries, staging to serving, lakehouse integration, and tiered storage. Key integration patterns include direct queries for exploratory analysis, parallel reads for large datasets, Hive/Iceberg integration for lakehouse compatibility, and loading to MergeTree for production analytics. The document emphasizes the importance of understanding HDFS's architecture, optimal file handling, and performance considerations to maximize the effectiveness of this integration. It highlights that while HDFS is optimized for durable storage and batch processing, Clickhouse® enhances real-time analytical capabilities, thus offering a balanced approach to managing lakehouse data with both durability and low-latency query performance.

ClickHouse®'s integration with Google Cloud Storage (GCS) addresses key challenges in modern data analytics by enabling seamless querying of files, loading data into low-latency MergeTree tables, exporting results back to GCS, and utilizing GCS for remote storage or backups. The integration, which uses the XML API with HMAC credentials instead of the more common JSON API, is pivotal as organizations increasingly separate compute from storage to optimize costs and maintain high query performance. GCS offers a cost-effective solution for storing historical data, allowing ClickHouse® to perform rapid sub-100ms queries over GCS-stored data. The integration supports various use cases, including data lake queries, incremental loading, tiered storage, and event-driven ingestion, and it provides comprehensive guidance on authentication, configuration, and operational best practices. By leveraging Parquet format for efficient data handling and implementing caching strategies, ClickHouse® enhances query performance over remote storage. Additionally, advanced ingestion models like S3Queue and Pub/Sub notifications facilitate continuous and event-driven data processing, making the setup particularly valuable for real-time and IoT applications. This integration ultimately achieves a balance between storage cost efficiency and analytical performance, allowing teams to optimize their infrastructure for both durability and speed.

Apache Iceberg is an open table format that offers ACID transactions and schema evolution, making it a preferred choice for large-scale analytics, while ClickHouse is a standard for real-time analytics. Integrating these two allows direct querying of Iceberg tables from ClickHouse, eliminating the need for complex ETL processes. However, achieving optimal performance requires understanding ClickHouse's interaction with Iceberg metadata, selecting appropriate table functions and engines, and optimizing table structure for query latency. The guide discusses various strategies, such as partitioning, sorting, and using materialized views, to enhance performance. It advises a hybrid approach, where Iceberg serves as the data lake and source of truth, while frequently accessed data is copied into ClickHouse for fast real-time queries. This combination leverages Iceberg's schema flexibility and ClickHouse's query speed but involves tradeoffs, particularly in balancing data freshness against query performance.

IBM, historically a powerhouse in the tech industry, has remained relevant by adapting to the evolving landscape, especially as it relates to cloud computing and open-source technology. Despite being late to the cloud era, IBM's focus on serving banks and large enterprises allowed it to maintain a stronghold in the industry. The company's acquisitions, such as Red Hat and Confluent, underscore its strategy to secure a dominant position in enterprise-level technology solutions, including hybrid cloud and streaming data services. IBM's longstanding commitment to open-source contributions, evident in its involvement with projects like Linux, Docker, Kubernetes, and Node.js, has been a key factor in its adaptability. The acquisition of Confluent, a major player in the Kafka ecosystem, positions IBM to leverage Kafka's integral role in data architecture, while maintaining the project's open-source vitality. IBM's strategic moves reflect its intent to capitalize on the upsell potential within data architecture solutions, ensuring its continued relevance and appeal to large enterprises.