Apache Parquet
Apache ORC
Apache Spark
Apache Kudu
Apache Arrow
MongoDB
Amazon S3
Amazon EMR
Panoply
Supermetrics
QuickBI
Fivetran
Airbyte
Snowflakepowe.red
Google BigQuery
Amazon Redshift
Panoply is a smart data warehouse that automates all three key aspects of the data analytics stack: data collection & transformation (ETL), database storage management, and query performance optimization. Panoply empowers anyone working with data analytics to quickly gain actionable insights on their own - without the need of IT and Engineering.
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Based on our record, Apache Parquet should be more popular than Panoply. It has been mentiond 19 times since March 2021. 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.
The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json. Source: 5 months ago
Apache Parquet (Parquet for short), which nowadays is an industry standard to store columnar data on disk. It compress the data with high efficiency and provides fast read and write speeds. As written in the Arrow documentation, "Arrow is an ideal in-memory transport layer for data that is being read or written with Parquet files". - Source: dev.to / 11 months ago
Googling that suggests this page: https://parquet.apache.org/. Source: about 1 year ago
You should also consider distribution of data because in a company that has machine learning workflows, the same data may need to go through different workflows using different technologies and stored in something other than a data warehouse, e.g. Feature engineering in Spark and loaded/stored in binary format such as Parquet in a data lake/object store. Source: about 1 year ago
This section will teach you how to read and write data to and from a variety of file types, including CSV, Excel, SQL, HTML, Parquet, JSON etc. You’ll also learn how to manipulate data from other sources, such as databases and web sites. Source: about 1 year ago
The service I used was Panoply. https://panoply.io/. Source: almost 2 years ago
Instead of building everything from scratch, you can try https://panoply.io/. Source: almost 2 years ago
Thanks will check this out. Currently we are testing with https://panoply.io/ but it's expensive. Source: over 2 years ago
Apache ORC - Apache ORC is a columnar storage for Hadoop workloads.
Supermetrics - Supermetrics condenses all the major vectors of data relevant to a user's marketing campaigns and helps them make sense of it all.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
QuickBI - Export data from over 300 sources to a data warehouse and analyze it with a reporting tool of your choice. Quick and easy setup.
Apache Kudu - Apache Kudu is Hadoop's storage layer to enable fast analytics on fast data.
Fivetran - Fivetran offers companies a data connector for extracting data from many different cloud and database sources.
