Jupyter
Looker
Databricks
Google BigQuery
Presto DB
Rakam
Informatica
Concurrent
Apache ORC
Apache Parquet
Amazon EMR
Apache Kudu
Impala
SQream
RJ Metrics
RocksDB
Jupyter Apache ORCNo Apache ORC videos yet. You could help us improve this page by suggesting one.
Based on our record, Jupyter seems to be a lot more popular than Apache ORC. While we know about 205 links to Jupyter, we've tracked only 3 mentions of Apache ORC. 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.
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 / 12 days ago
Jupyter Lab web-based interactive development environment. - Source: dev.to / 22 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 / 17 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 / 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 / 2 months ago
The information can be stored in a database or as files, serialized in a standard format and with a schema agreed with your Data Engineering team. Depending on your information and requirements, it can be as simple as CSV, XML or JSON, or Big Data formats such as Parquet, Avro, ORC, Arrow, or message serialization formats like Protocol Buffers, FlatBuffers, MessagePack, Thrift, or Cap'n Proto. - Source: dev.to / over 1 year ago
Data formatting is another place to make gains. When dealing with huge amounts of data, finding the data you need can take up a significant amount of your compute time. Apache Parquet and Apache ORC are columnar data formats optimized for analytics that pre-aggregate metadata about columns. If your EMR queries column intensive data like sum, max, or count, you can see significant speed improvements by reformatting... - Source: dev.to / over 2 years ago
The following stack captures layers of software components that make up Hudi, with each layer depending on and drawing strength from the layer below. Typically, data lake users write data out once using an open file format like Apache Parquet/ORC stored on top of extremely scalable cloud storage or distributed file systems. Hudi provides a self-managing data plane to ingest, transform and manage this data, in a... - Source: dev.to / almost 3 years ago
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.
Apache Parquet - Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem.
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.What is Apache Spark?
Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Apache Kudu - Apache Kudu is Hadoop's storage layer to enable fast analytics on fast data.
