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.
Based on our record, Jupyter seems to be a lot more popular than SQream. While we know about 205 links to Jupyter, 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.
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
JupyterLab: JupyterLab is an interactive development environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It's particularly well-suited for data science and research-oriented projects. - Source: dev.to / 1 day ago
Jupyter Lab web-based interactive development environment. - Source: dev.to / 12 days ago
Choosing IDE: Selecting a suitable Integrated Development Environment (IDE) is crucial for efficient coding. Consider popular options such as PyCharm, Visual Studio Code, or Jupyter Notebook. Install your preferred IDE and ensure it's configured to work with Python. - Source: dev.to / 7 days ago
Jupyter Notebooks is very popular among data people specially Python users. So, I tried to find a way to run the Groovy kernel inside a Jupyter Notebook, and to my surprise, I found a way, BeakerX! - Source: dev.to / about 2 months ago
Note. Nowadays, there are many flavors of notebooks (Jupyter, VSCode, Databricks, etc.), but they’re all built on top of IPython. Therefore, the Magics developed should be reusable across environments. - Source: dev.to / about 2 months ago
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
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
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.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.