Fivetran VS Apache Flink

Fivetran Reviews

Apache Flink Reviews

Fivetran mentions (12)

• Sync Snowflake and Google Sheets

  Even looking past these limitations, internal scripts invariably require development and maintenance time, and as any developer knows, can break at the worst of times :) Method #2: Use Zapier? (https://zapier.com) Want to use Zapier to do this? You can’t. Not only because it doesn’t track deletes, updates to existing records, and only does one way syncs. But because Snowflake isn’t supported. Method #3:... - Source: Hacker News / about 1 year ago

• Big problem with companies now is they hire data scientist for task that don't require data science practices.

  Disclaimer: I work for Fivetran, a data integration company. Source: over 1 year ago

• I love data science but hate data engineering

  Disclaimer: I'm a product evangelist for a data integration company called Fivetran, so I'm shamelessly shilling here. Source: almost 2 years ago

• Which webflow theme is this?

  I really like the theme they’re using on https://fivetran.com. Source: almost 2 years ago

• A modern data stack for startups

  From experience then, believe me when I say you don't want to build these. Thankfully, ETL products like Fivetran and Stitch run and maintain these extraction processes for you. - Source: dev.to / about 2 years 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 / 23 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