Based on our record, Amazon Redshift should be more popular than Apache Storm. It has been mentiond 29 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 several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 2 years ago
Although this article lists a lot of targets for technical selection, there are definitely others that I haven't listed, which may be either outdated, less-used options such as Apache Storm or out of my radar from the beginning, like JAVA ecosystem. - Source: dev.to / over 2 years ago
Storm, a system for real-time and stream processing. - Source: dev.to / over 2 years ago
Google has scaled well and has helped others scale, Twitter has always been behind by years. I think the only thing they did well was Twitter Storm, now taken up by Apache Foundation. Source: over 2 years ago
Streaming: Sparks Streamings's latency is at least 500ms, since it operates on micro-batches of records, instead of processing one record at a time. Native streaming tools like Storm, Apex or Flink might be better for low-latency applications. - Source: dev.to / over 3 years ago
If your team is managing large volumes of historical data using platforms like Snowflake, Amazon Redshift, or Google BigQuery, you’ve probably noticed a shift happening in the data engineering world. A new generation of data infrastructure is forming — one that prioritizes openness, interoperability, and cost-efficiency. At the center of that shift is Apache Iceberg. - Source: dev.to / 27 days ago
Postgres can be easily adapted to build highly tailored solutions. For instance, Amazon Redshift can be considered a highly scalable fork of Postgres. It’s a distributed database focusing on OLAP workloads that you can deploy in AWS. - Source: dev.to / 6 months ago
With the transition from ETL to ELT, data warehouses have ascended to the role of data custodians, centralizing customer data collected from fragmented systems. This pivotal shift has been enabled by a suite of powerful tools: Fivetran and Airbyte streamline the extraction and loading, DBT handles the transformation, and robust warehousing solutions like Snowflake and Redshift store the data. While traditionally... - Source: dev.to / 7 months ago
They differ from conventional analytic databases like Snowflake, Redshift, BigQuery, and Oracle in several ways. Conventional databases are batch-oriented, loading data in defined windows like hourly, daily, weekly, and so on. While loading data, conventional databases lock the tables, making the newly loaded data unavailable until the batch load is fully completed. Streaming databases continuously receive new... - Source: dev.to / about 1 year ago
Data warehousing is the process of storing and analyzing large volumes of data for business intelligence and analytics purposes. AWS offers a fully managed data warehousing service called Amazon Redshift that can handle petabyte-scale data warehouses with ease. - Source: dev.to / over 1 year ago
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Microsoft SQL Server - Microsoft Azure is an open, flexible, enterprise-grade cloud computing platform. Move faster, do more, and save money with IaaS + PaaS. Try for FREE.
Vertica - Vertica is a grid-based, column-oriented database designed to manage large, fast-growing volumes of...
Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.