August 2026 Summaries

Graylog and ClickHouse serve complementary roles in a log analytics architecture: Graylog, backed by OpenSearch, remains responsible for stream routing, extractors, dashboards, alerts, and operator search, while ClickHouse provides lower-cost long-term retention, typed event storage, SQL analytics, joins with product data, and customer-facing APIs. Because Graylog has no native ClickHouse output, teams must use explicit ingestion paths such as producer-side GELF or HTTP dual-writing, a post-processing pipeline forwarder through HTTP or Kafka, or batch replay from archived index data, with many deployments combining live dual-write and archive backfill. The integration depends on defining a shared field contract and KPI definitions so Graylog views and ClickHouse rollups count events consistently, while ClickHouse schemas should use stable typed columns, bounded dimensions in sort keys, and maps or JSON for high-cardinality attributes. The guidance cautions against copying OpenSearch mappings directly, forwarding unprocessed GELF, replaying archives without deduplication, and allowing dashboards and analytics systems to use conflicting filters. Managed ClickHouse services such as Tinybird can add ingestion connectors, API endpoints, tenant-scoped access, and schema workflows, but the central approach is to retain Graylog for operational log management and use ClickHouse for scalable analytics and extended retention.

Grafana Tempo and ClickHouse are presented as complementary trace-data systems: Tempo stores OpenTelemetry traces efficiently in object storage for Grafana-based trace investigation and TraceQL queries, while ClickHouse stores denormalized span records and rollups for long-term SQL analytics, cross-signal joins, SLO calculations, and product-facing APIs. Because Tempo does not natively export spans to ClickHouse, the recommended architecture uses an OpenTelemetry Collector or Grafana Alloy to fan out incoming OTLP data to both backends after applying memory limits, attribute normalization and secret redaction, tail sampling, and batching. The guidance emphasizes centralized sampling before dual writes to control cost, retaining errors and slow traces while sampling successful traffic and accounting for sampling rates in dashboard metrics. It recommends a ClickHouse schema ordered around service, operation, and timestamp, with high-cardinality values in attributes, plus minute-level materialized rollups to avoid expensive raw-span scans. Teams are advised to assign ownership of each KPI between Tempo-generated metrics and ClickHouse rollups, backfill historical data from Tempo blocks with deduplication, and validate metric parity across systems. Tinybird is described as an optional managed ClickHouse layer for ingestion, SQL endpoints, real-time APIs, scoped access, and testing data-model changes.

Sentry and ClickHouse serve complementary roles in an error-monitoring architecture: Sentry manages issue grouping, stack-trace deduplication, developer alerts, assignments, and release regressions, while ClickHouse retains row-level error and transaction data for long-term SQL analytics, release comparisons, product metrics, and customer-facing reliability APIs. Because Sentry does not automatically archive all events to ClickHouse, teams can use SDK dual-writing, a self-hosted Relay plus mirroring sidecar, or batch exports for backfills, ensuring that ClickHouse ingestion never blocks Sentry reporting. The recommended design routes Envelope items into separate error and transaction tables, preserves metadata such as fingerprints, issue IDs, releases, environments, and exception types, and avoids attempting to reproduce Sentry’s issue-grouping logic in SQL. Operational guidance emphasizes asynchronous ingestion, post-scrubbing payload consistency, appropriate MergeTree sort keys, parity checks between Sentry and ClickHouse release-health metrics, and limited storage of full stack traces. Tinybird is presented as an optional managed ClickHouse layer for high-volume ingestion, SQL endpoints, release rollups, and tenant-scoped embedded dashboards.

StarRocks is an Apache 2.0 open-source analytical database with no software license fee, but its total cost depends on whether it is self-hosted, run through CelerData’s managed service, or deployed via a cloud offering such as Alibaba Cloud EMR Serverless StarRocks. Costs arise from provisioned compute, local disks or object storage, network transfer, high-availability node requirements, backups, and operational labor, with production self-hosted clusters commonly requiring at least three frontend and three backend nodes. Its shared-nothing architecture uses backend nodes with replicated local storage to favor lower query latency, while shared-data separates compute from object storage to make storage scaling cheaper but potentially increase query-compute costs for broad scans. Alibaba’s serverless offering illustrates usage-based billing through compute units, ESSD disks, backend node capacity, and data storage, whereas CelerData generally provides custom managed-service quotes. Materialized views can reduce repeated query scans, but ingestion, compaction, upserts, replication, egress, cross-availability-zone traffic, and engineering support can materially affect spending. Budgeting should account for architecture, storage growth, query patterns, SLA-driven redundancy, and whether a team needs only an OLAP database or a managed analytics platform with APIs, ingestion, and deployment tooling.

Choosing between self-hosted, conventional managed, and Tinybird-based ClickHouse primarily determines who operates the production environment, responds to incidents, and executes capacity changes rather than which SQL engine is used. Self-hosting provides complete control over hardware, topology, configuration, and data residency, but requires teams to manage replicated storage, ClickHouse Keeper, load balancing, merges, upgrades, backups, monitoring, and on-call response; replication also multiplies storage costs and operational complexity. Conventional managed providers such as ClickHouse Cloud, Altinity.Cloud, and Aiven handle infrastructure, backups, and much of version maintenance, while customers still own schema design, ingestion behavior, API layers, authentication, application reliability, and many scaling decisions. Tinybird adds managed ingestion, SQL-backed HTTP APIs, Git-based deployment workflows, preview environments, observability, and workload isolation around ClickHouse, while offering shared infrastructure for simpler use cases, dedicated clusters with selectable replica counts and traffic weights, and a self-managed option for customers with policy or residency requirements. Across all approaches, poor partitioning or insert batching can still cause issues such as excessive parts, but the operating models differ substantially in who must diagnose and remediate those problems. Cost comparisons should account not only for compute and storage but also for engineering time, upgrades, incidents, and the expertise needed to run a reliable ClickHouse platform.

ClickHouse cluster operations encompass adding or removing replicas, routing read and write workloads, resizing hardware, upgrading versions, managing replication and merges, and handling shards, with the central operational question being who can make changes and who bears responsibility when they fail. In self-hosted deployments, teams must manage Keeper, replica synchronization, load balancing, metadata cleanup, and risks such as replication lag, excessive data parts, and the lack of built-in online resharding; replicas duplicate data while shards divide it. Conventional managed offerings reduce infrastructure administration but still require customers to manage application behavior, data modeling, batching, and capacity policies. Tinybird’s dedicated Cluster Management feature is presented as a model in which customers choose replica counts, sizes, and traffic weights while Tinybird provisions, replicates, and operates the underlying cluster, with UI and API controls for adding, draining, removing, and routing traffic across replicas. The feature uses separate read, write, and copy-job weights with safeguards requiring at least one active read and write destination, but it is not autoscaling, on-demand sharding, or a replacement for query and schema optimization.

Choosing a ClickHouse setup should begin with the operating model—who owns the environment, incident response, and production changes—rather than only the deployment location such as VMs, Docker, Kubernetes, or a managed service. Self-managed ClickHouse gives teams full control but requires them to operate infrastructure, Keeper, replication, capacity changes, failures, and resharding, while Kubernetes operators automate deployment tasks without removing operational responsibility. Conventional managed providers such as ClickHouse Cloud, Altinity.Cloud, and Aiven run core database infrastructure, although customers still manage schemas, query load, ingestion, APIs, and sizing decisions. Tinybird shared infrastructure focuses on a managed product layer for event ingestion, SQL pipelines, and API endpoints, with Tinybird handling scaling and replica operations, whereas Tinybird dedicated infrastructure adds isolated hardware and optional Cluster Management, allowing customers to select replica counts, SKUs, and traffic weights while Tinybird provisions, replicates, upgrades, and repairs the platform. The recommended choice depends on compliance requirements, desired infrastructure isolation, need for replica-level control, existing operational expertise, and whether the priority is operating a database cluster or rapidly delivering data-backed APIs.

New Relic and ClickHouse are presented as complementary components of a hybrid telemetry architecture: New Relic supports short-retention operational monitoring, NRQL alerts, entity views, and incident investigation, while ClickHouse or managed ClickHouse service Tinybird provides longer-term storage, SQL aggregation, joins, and customer-facing analytics for logs and traces. Because New Relic’s default retention windows range from days to a few months, teams needing 6–13 months of queryable data should fan out telemetry at its source through asynchronous, compressed, batched Log API dual-writing, Kafka, or OpenTelemetry Collector multi-export rather than attempting to use NerdGraph as a live ingestion mechanism. The approach recommends assigning NRQL ownership of short-window paging and ClickHouse ownership of long-window reporting to avoid duplicate alerts, maintaining typed schemas and attribute allowlists, separating logs, traces, and metrics into distinct tables, and validating parity between systems. NerdGraph historical exports and Data Plus object-storage exports can support backfills or continuous replication, but have constraints and should not replace streaming ingestion. The guidance also cautions against synchronous logging, uncompressed requests, duplicate New Relic and OpenTelemetry instrumentation in a single process, and aggregated export queries, while positioning Tinybird as an option for managed ClickHouse-based product and security analytics.

Splunk–ClickHouse integrations are presented as a dual-index architecture in which Splunk remains the primary tool for keyword search, incident investigation, SPL dashboards, and short-term operational workflows, while ClickHouse supports lower-cost SQL aggregations, longer retention, cross-source joins, embedded dashboards, and product-facing APIs. Because Splunk does not natively forward data to ClickHouse, the integration requires an explicit bridge: application logs can be dual-written through Splunk HTTP Event Collector and ClickHouse-compatible HTTP ingestion, infrastructure logs can be cloned from universal forwarders through heavy forwarders and Kafka, and historical data can be replayed from S3-compatible archives. The approach depends on a shared field contract that maps Splunk metadata and raw events into typed ClickHouse columns, normalizes timestamps and schemas, preserves raw data for audit purposes, and verifies parity between Splunk and ClickHouse counts to detect drift. The text recommends materialized rollups, schema validation and quarantine handling, retention tiers spanning Splunk hot data, ClickHouse analytics data, and long-term object storage, while warning against placing HEC on universal forwarders, relying only on raw log fields, overlooking time-zone differences, assuming native forwarding exists, and extracting excessive high-cardinality fields. Tinybird is described as a managed ClickHouse option for teams seeking streaming ingestion and SQL APIs without operating the underlying pipeline.

Elasticsearch and Kibana are positioned as the search and operational interface for Elastic data streams, while ClickHouse is presented as a complementary columnar analytics store for long-retention SQL queries, joins, rollups, and APIs. The recommended architecture fans out events at collection time through Elastic Agent, Beats, Logstash, or Kafka rather than repeatedly scrolling Elasticsearch, with Elasticsearch retained for Kibana and security workflows and ClickHouse receiving a parallel stream. Successful integration depends on mapping Elastic Common Schema fields into typed ClickHouse MergeTree columns, partitioning by event time, sorting around common analytical filters such as service and log level, retaining raw JSON for less common fields, and applying TTL policies. The guidance covers Kafka-based ingestion, Logstash dual outputs, controlled Elasticsearch backfills, consumer lag and failure handling, quarantine tables for malformed events, persistent queues, and parity and latency tests before deployment. It also advises using ClickHouse for cross-domain joins and long-range aggregations while keeping Kibana disconnected from ClickHouse, and describes Tinybird as a managed ClickHouse option that can consume the same event fan-out and expose SQL-backed APIs without operating consumer and merge infrastructure.

Datadog and ClickHouse can be integrated as complementary systems, with Datadog handling real-time incident response, monitoring, indexed log search, traces, and on-call workflows, while ClickHouse provides cost-effective long-term retention, high-volume analytical SQL, product-data joins, compliance analysis, and customer-facing analytics. The recommended architecture uses Observability Pipelines Workers for live dual-writing of processed, redacted, and normalized telemetry to both platforms, cloud Log Archives plus a custom event-driven loader for delayed compliance storage and backfills, and Datadog APIs only for prototypes, one-time exports, or aggregate synchronization. Effective implementations require explicit schemas, bounded-dimension ClickHouse sort keys, deduplication, checkpointed API pagination, materialized rollups for long-range queries, and a documented ownership model for every KPI so Datadog monitoring definitions and ClickHouse analytics remain consistent. Teams should validate ingestion, archive replay, rate-limit handling, query performance, and daily parity between systems before production, while avoiding common mistakes such as treating archives as real-time feeds, using high-cardinality identifiers in sort keys, or maintaining conflicting error-rate definitions. Federated Logs can let Datadog Log Explorer query ClickHouse-hosted logs without re-ingestion, and managed ClickHouse services such as Tinybird may simplify ingestion, schema changes, and serving SQL-backed APIs for product use cases.

ClickHouse is presented as a suitable platform for telecom analytics at very high CDR and session-record volumes because its columnar MergeTree storage, compression, partitioning, and aggregation-oriented query model support fast analysis of append-only network data. The recommended architecture separates voice, SMS, data-session, signaling, and radio-probe records into dedicated fact tables, normalizes vendor feeds through mediation, and ingests data primarily through Kafka microbatches or batch loads rather than single-row inserts. Schema and sort-key design should reflect distinct access patterns, with NOC dashboards optimized around region, service, result, and time while subscriber-centric queries may require separate tables. The approach supports operational KPIs such as drop rates, answer-seizure ratios, attach failures, congestion correlation, APN behavior, interconnect reconciliation, fraud detection, and roaming analysis, while materialized hourly rollups and table-specific retention policies preserve longer-term reporting without scanning raw data. Key operational concerns include monitoring ingest and Kafka lag, preventing excessive table parts, handling mediation replays through versioned deduplication, avoiding FINAL for real-time serving, and validating rollup parity and query pruning before production use. The piece also describes Tinybird as a managed ClickHouse option that combines streaming ingestion, SQL transformations, authenticated APIs, schema migration workflows, and monitoring for teams seeking to reduce cluster-management responsibilities.

ClickHouse can support large-scale time-series analytics despite not being a dedicated TSDB by using columnar storage, compression, MergeTree engines, carefully selected sort keys, and materialized-view rollups. The approach recommends narrow metric schemas for high-cardinality telemetry, distinguishes counter rates from gauge aggregations, batches ingestion efficiently, stores timestamps consistently, and uses tiered raw, minute, hourly, and daily tables with TTL-based retention to balance drill-down detail against long-range query cost. It describes SQL patterns for latest values, percentile latency, SLO monitoring, window comparisons, gap handling, late data, duplicate delivery, tenant-scoped APIs, and query-log analysis, while emphasizing that dashboard filters should drive partitioning and sort-key design. ClickHouse is positioned as especially useful for long-term analytics combining metrics with logs, events, and dimensions, whereas dedicated TSDBs remain preferable for native PromQL, service discovery, and scrape-centric operations; managed platforms such as Tinybird can reduce operational work by providing ingestion, transformations, APIs, and schema-management features.

Amazon Redshift and ClickHouse are positioned as complementary systems for different analytical workloads: Redshift is an AWS-oriented MPP warehouse suited to governed, curated dimensional models, scheduled ETL, finance reporting, and batch BI, while ClickHouse is designed for high-volume event ingestion, high-cardinality queries, and low-latency dashboards or APIs. The comparison becomes relevant when a Redshift deployment originally built for reporting begins supporting frequently refreshed product dashboards or direct API traffic, which can create WLM queue contention, concurrency-scaling costs, and added caching or middleware complexity. A common hybrid architecture sends raw streaming events to ClickHouse for real-time operational analytics and product-facing endpoints, while exporting curated hourly or daily aggregates through S3 into Redshift for finance, compliance, and executive reporting. Successful migrations are presented as targeted moves of serving workloads rather than wholesale warehouse replacements, involving backfills from S3, streaming ingestion, materialized rollups, query parity checks, and data models optimized for ClickHouse sort keys rather than replicated Redshift star schemas. Redshift Spectrum and Serverless can support flexible lake queries and variable BI demand but do not eliminate the latency and cost constraints of using a batch warehouse for fresh, high-concurrency APIs; managed ClickHouse services such as Tinybird are described as options for reducing operational and API-layer overhead.

Prometheus and ClickHouse use different data models, so reliable integrations require explicit metric schemas, bounded labels, efficient rollups, and a clear choice of the system that owns each alert. Four main approaches are described: exposing SQL-computed Tinybird Pipes in Prometheus exposition format for scraping, querying ClickHouse directly from Grafana, remote-writing Prometheus or OpenTelemetry samples into ClickHouse for long-term SQL analytics, and scraping Tinybird workspace telemetry for platform monitoring. Prometheus-format Pipe endpoints must return numeric values with metric names, optional help and type metadata, and carefully controlled label maps, while scrape authentication, endpoint validity, and query latency should be tested before deployment. Direct Grafana-to-ClickHouse queries simplify operations when Prometheus-native features are unnecessary, whereas remote write supports long retention but requires adapters, deduplication, batching, backpressure controls, and materialized rollups. The guidance emphasizes excluding high-cardinality identifiers such as user IDs, trace IDs, and raw URLs from labels, using aggregated data rather than raw samples for long dashboard ranges, and avoiding duplicate definitions of the same KPI across systems. It recommends keeping infrastructure metrics and short-term PromQL rules in Prometheus, computing metrics that require SQL joins in ClickHouse or Tinybird, selecting one canonical source for each alert, and validating schemas, format, authentication, cardinality, load behavior, failures, and configuration drift through acceptance tests.

Hevo Data is a managed ELT pipeline product that facilitates the connection of data sources, allowing for scheduled or change data capture (CDC) replication to a destination, such as ClickHouse®, which is particularly effective for interactive analytics. To ensure seamless integration and avoid inconsistencies, it's crucial to establish a load contract that defines the creation permissions, update processes, and query conditions after merges. Users should confirm the capabilities of their selected destination and understand the specifics of how Hevo operates within their plan, including whether ClickHouse® is a native destination or requires an intermediary like S3. Hevo is particularly suitable for teams needing numerous SaaS and database sources without building connectors, with a focus on UI-driven pipelines rather than comprehensive solutions like Airflow. For effective pipeline management, it's vital to write a load contract, manage table designs, and understand the implications of different pipeline modes. Additionally, the choice between transforming data within Hevo or post-landing in ClickHouse® can impact the efficiency and clarity of data models, highlighting the importance of tailoring strategies to specific needs, such as using append-only event tables for immutable data. Effective schema management, connectivity configurations, and understanding of Hevo’s capabilities relative to alternatives like Fivetran and Stitch are essential to optimize performance and ensure data reliability.

The text provides a comprehensive overview of integrating Confluent Cloud's managed Kafka with ClickHouse for efficient data streaming and analytics handling. It outlines the challenges and strategies for setting up such an integration, emphasizing the importance of consumer choices, authentication methods, schema strategies, and lag service level agreements (SLAs). The guide discusses different consumer shapes, such as using ClickHouse's Kafka engine, Kafka Connect sink, or Tinybird, based on network setup and operational requirements. It highlights the necessity of having a dedicated Confluent principal for topic access, establishing a clear network dependency path, and maintaining destination tables with appropriate ordering. The text also underscores the need for a written freshness budget, alerts for consumer lag and errors, and a plan for handling problematic messages. Furthermore, it explores the roles of schema registry and partition design, and provides solutions for common failure modes, ensuring a robust and reliable data pipeline between Confluent and ClickHouse.

OpenTelemetry standardizes the process of emitting traces, metrics, and logs while leaving the choice of storage for analytical queries open, with ClickHouse® being a popular option for handling extensive data needs. A successful integration between OpenTelemetry and ClickHouse® relies heavily on the design of the OpenTelemetry Collector, which must correctly configure receivers, processors, exporters, and schemas for each signal. Proper setup results in fast and efficient data handling, whereas mistakes can lead to costly and ineffective data archives. The text emphasizes the importance of maintaining separate collector pipelines for different signals, implementing sampling and filtering rules, and managing cardinality to avoid overwhelming the system. It also discusses the appropriate database layout and retention strategies, advocating for separate databases per environment and a focus on rollups rather than raw data to satisfy long-term data needs. Additionally, it highlights the significance of treating collector configurations with the same rigor as application code to ensure stability and security, suggesting that Tinybird can be used for OpenTelemetry analytics APIs when real-time data ingestion paths are required.

The newly introduced Overview page and organization-wide Logs explorer in Tinybird have become generally available, offering enhanced visibility and accessibility across an organization for all members, not just administrators. As Tinybird setups expand, understanding the entire infrastructure becomes crucial, and these tools address that need by providing a centralized point of access to monitor usage, infrastructure metrics, and recent errors. The Overview page acts as a comprehensive entry point summarizing workspace activities, usage metrics, and errors, while the Logs explorer aggregates logs from all workspaces, allowing for efficient troubleshooting and error investigation. This development transforms visibility into a shared responsibility among team members, enabling developers and other personnel to independently check resource statuses and diagnose issues without waiting for administrative intervention. Despite this broadened access, administrative and sensitive operations remain restricted to organization admins, ensuring a balance between visibility and control. These new features mark a shift from purely administrative tasks to a more collaborative monitoring and troubleshooting approach, with further improvements planned to streamline the process from problem detection to root cause identification.

Segment serves as a customer data pipeline, efficiently collecting and routing event streams and traits, while ClickHouse® is utilized for aggregating and serving data at high speed for interactive analytics. A successful integration between Segment and ClickHouse® requires a clear separation of roles: Segment should not be treated as a database, nor should ClickHouse® be used as an identity graph. Various integration paths include using Segment's warehouse destinations with ClickHouse® for reliable data syncing, employing Segment Functions or webhooks for fresher data, and leveraging object storage as a buffer. Proper schema management, such as using ReplacingMergeTree for safe reprocessing and promoting relevant properties, ensures efficient data handling. The integration strategy should accommodate data freshness, identity management, and property promotion while considering security and cost implications, ensuring that Segment's role as a data collection and activation fabric complements ClickHouse®'s analytical capabilities.

A common issue among data teams using ClickHouse for scaling analytical pipelines is treating it as a transactional database, leading to repeated mistakes that hinder performance. To address these, it's crucial to differentiate between OLTP and OLAP databases; ClickHouse, an OLAP database, is optimized for complex reads across vast data sets. Key strategies include filtering data early to minimize unnecessary processing, leveraging sorting keys for efficient data retrieval, performing data transformations during ingestion instead of at query time, and defining data types accurately to reduce computational overhead. Joins should be optimized by filtering and reducing data before combining, and materialized views can be used to precompute frequent transformations. Proper partitioning and the use of tools like EXPLAIN and query logs are essential for continuous monitoring and optimization, aiming to minimize resource consumption by reducing unnecessary data reads and calculations. Through these optimizations, data teams can significantly decrease the computational resources required, exemplified by a case where a startup reduced its instance capacity needs by two-thirds, highlighting the importance of making ClickHouse perform less redundant work.

The integration of ClickHouse® and Redpanda provides a high-performance solution for event streaming without the overhead of the Java Virtual Machine (JVM), offering Kafka API compatibility with simplified operations. Redpanda's advantages include lower latency and easier operations compared to Apache Kafka, while maintaining full API compatibility, and it eliminates the need for external dependencies like ZooKeeper. The integration allows ClickHouse® to consume events using its native Kafka table engine, enabling real-time analytics with predictable performance due to its C++ implementation and thread-per-core architecture. However, successful deployment requires understanding various operational complexities, such as consumer parallelism limits, offset management strategies, and error handling patterns, which differ from traditional Kafka ecosystems. An alternative to managing these complexities is using Tinybird, a managed real-time data platform that eliminates the need for streaming infrastructure by directly connecting streaming sources and providing real-time analytics as production APIs, thereby simplifying operations and ensuring consistent performance.