ComplyCube
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Databricks
Google Cloud Dataproc
Snowflake
Qubole
HortonWorks Data Platform
SQream
GridGain In-Memory Data Fabric
Panoply
Apache ORC
RJ Metrics
Impala
Fujitsu Symfoware Analytics Server
Microsoft SQL
SQream is a data analytics acceleration platform built especially for massive data - from terabytes to petabytes. SQream takes queries down from days to hours and hours to minutes. The SQream platform provides the ability to analyze more data, faster, with multiple dimensions and cuts data preparation significantly by enabling ad-hoc querying on raw data. Leading global organizations in telecommunications, healthcare, ad-tech, retail and more rely on SQream to achieve critical business insights and potentially valuable BI across their massive data stores.
Amazon EMRBased on our record, Amazon EMR should be more popular than SQream. It has been mentiond 10 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.
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
Later on, when your needs will increase, you can work with https://sqream.com/ (Panoply was acquired by SQream DB). Source: almost 2 years ago
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
GridGain In-Memory Data Fabric - TheGridGain In-Memory Computing Platform is a comprehensive solution provides speed and scale for data intensive applications across any data store
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Panoply - Panoply is a smart cloud data warehouse
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.What is Apache Spark?
Apache ORC - Apache ORC is a columnar storage for Hadoop workloads.