Apache Spark
Apache Flink
Hadoop
Apache Storm
Apache Kafka
Apache Hive
Google Cloud Dataflow
Spring Batch
SQream
Panoply
Impala
Apache ORC
Google BigQuery
GridGain In-Memory Data Fabric
Amazon EMR
QuickBI
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.
Apache Spark SQreamNo features have been listed yet.
Based on our record, Apache Spark seems to be a lot more popular than SQream. While we know about 70 links to Apache Spark, we've tracked only 1 mention of SQream. 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.
Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / about 1 month ago
Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / about 1 month ago
One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 2 months ago
[1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 2 months ago
If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
Later on, when your needs will increase, you can work with https://sqream.com/ (Panoply was acquired by SQream DB). Source: about 3 years ago
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Panoply - Panoply is a smart cloud data warehouse
Hadoop - Open-source software for reliable, scalable, distributed computing
Impala - Impala is a modern, open source, distributed SQL query engine for Apache Hadoop.
Apache Storm - Apache Storm is a free and open source distributed realtime computation system.
Apache ORC - Apache ORC is a columnar storage for Hadoop workloads.