tape
Apache Orc
| tape | Apache Orc | |
|---|---|---|
| - | 4 | |
| 2,460 | 654 | |
| 0.0% | 0.6% | |
| 0.0 | 9.5 | |
| about 1 year ago | 3 days ago | |
| Java | Java | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tape
We haven't tracked posts mentioning tape yet.
Tracking mentions began in Dec 2020.
Apache Orc
-
Java Serialization with Protocol Buffers
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.
- Personal data of 120,000 Russian servicemen fighting in Ukraine made public
-
AWS EMR Cost Optimization Guide
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 data like CSVs into one of these columnar formats.
-
Apache Hudi - The Streaming Data Lake Platform
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 way that unlocks incremental data processing on them.
What are some alternatives?
Big Queue - A big, fast and persistent queue based on memory mapped file.
Protobuf - Protocol Buffers - Google's data interchange format
Persistent Collection - A Persistent Java Collections Library
Apache Parquet - Apache Parquet
Wire - gRPC and protocol buffers for Android, Kotlin, Swift and Java.
Apache Avro - Apache Avro is a data serialization system.
Androl4b - A Virtual Machine For Assessing Android applications, Reverse Engineering and Malware Analysis
hudi - Upserts, Deletes And Incremental Processing on Big Data.
OpenKeychain - OpenKeychain is an OpenPGP implementation for Android.
Apache Thrift - Apache Thrift
Android-Templates-And-Utilities - Collection of source codes, utilities, templates and snippets for Android development.
debezium - Change data capture for a variety of databases. Please log issues at https://issues.redhat.com/browse/DBZ.