ClickHouse VS Apache Flink

ClickHouse Reviews

Apache Flink Reviews

ClickHouse mentions (43)

• The 2024 Web Hosting Report

  For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules. - Source: dev.to / 3 months ago

• Choosing Between a Streaming Database and a Stream Processing Framework in Python

  Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in... - Source: dev.to / 3 months ago

• Real-Time Data Enrichment and Analytics With RisingWave and ClickHouse

  To achieve seamless real-time data ingestion, transformation, and analytics, a powerful combination to explore is RisingWave and ClickHouse. RisingWave is a PostgreSQL-compatible database specifically designed for stream processing. It excels at ingesting real-time data streams, performing diverse transformations, and enabling instant querying of results. ClickHouse® is a high-performance, column-oriented SQL... - Source: dev.to / 4 months ago

• Ask HN: Is there a Hacker News takeout to export my comments / upvotes, etc.?

  You can export the whole dataset as described here: https://github.com/ClickHouse/ClickHouse/issues/29693

      curl https://clickhouse.com/ | sh.

  - Source: Hacker News / 5 months ago

• Best alternative

  Nowadays I am looking at the clickhouse and how it might help me maybe you can check it out: https://clickhouse.com/. Source: 5 months ago

Apache Flink mentions (27)

• Top 10 Common Data Engineers and Scientists Pain Points in 2024

  Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 21 days ago

• Choosing Between a Streaming Database and a Stream Processing Framework in Python

  Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago

• Go concurrency simplified. Part 4: Post office as a data pipeline

  Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 4 months ago

• Five Apache projects you probably didn't know about

  Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago

• Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog

  Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 5 months ago