Timeplus
Materialize
Apache Flink
KSQL
RisingWave
Apache Spark
Azure Stream Analytics
Amazon Kinesis
Apache Parquet
Apache Spark
Apache Arrow
Amazon S3
DuckDB
Apache Avro
Apache Kafka
Hugging Face
Ready to turn your real-time data into actions?
Timeplus Enterprise Self-Hosting: deploy on your data center or own cloud account Timeplus Proton: open-source core engine
It empowers developers to build powerful and reliable streaming analytics applications, at speed and scale, anywhere.
Timeplus Apache ParquetNo Apache Parquet videos yet. You could help us improve this page by suggesting one.
Based on our record, Apache Parquet seems to be a lot more popular than Timeplus. While we know about 31 links to Apache Parquet, we've tracked only 1 mention of Timeplus. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
* Proton is more developer friendly To explore Proton yourself, visit the [Proton GitHub repo](https://github.com/timeplus-io/proton). - Source: Hacker News / over 2 years ago
Apache Iceberg fits these requirements well. Iceberg stores data as immutable Apache Parquet files and adds them through atomic commits, so readers always see a consistent snapshot. A separate metadata layer prunes files by their statistics before the data itself is ever read, and those statistics can be extended to match an observability filtering profile. - Source: dev.to / 13 days ago
Depends on the domain. There's a bunch of sciences using large datasets served up efficiently using static file formats, e.g., https://zarr.dev/ and https://parquet.apache.org/. - Source: Hacker News / about 1 month ago
The data files themselves are still standard Parquet or ORC. The table format adds a metadata layer on top that gives those files the properties of a database table. - Source: dev.to / 2 months ago
The dataset is huge - in parquet conversion - it is total 9gb. And in raw PNG image nested folders - it is 67 gigabytes. Huge... - Source: dev.to / 4 months ago
The solution is to standardize on columnar formats like Apache Parquet. Parquet stores data in columns, not rows, which immediately enables column pruning. If a query is SELECT avg(price) FROM sales, the engine reads only the price column and ignores all others. This can reduce storage footprints by up to 75% compared to raw formats and is a cornerstone of modern analytics performance. - Source: dev.to / 8 months ago
Materialize - A Streaming Database for Real-Time Applications
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Apache Arrow - Apache Arrow is a cross-language development platform for in-memory data.
KSQL - Confluent KSQL is the streaming SQL engine that enables real-time data processing against Apache Kafkaยฎ.
Amazon S3 - Amazon S3 is an object storage where users can store data from their business on a safe, cloud-based platform. Amazon S3 operates in 54 availability zones within 18 graphic regions and 1 local region.