PostgreSQL 10.2, 9.6.7, 9.5.11, 9.4.16, and 9.3.21 released, Bokeh 0.12.14 released, Cloudera Altus Analytic DB Beta,and more in today’s top stories around machine learning, deep learning,and data science news.
1. PostgreSQL 10.2, 9.6.7, 9.5.11, 9.4.16, and 9.3.21 released!
PostgreSQL Global Development Group has released updates 10.2, 9.6.7, 9.5.11, 9.4.16, and 9.3.21. This release:
- Fixes two security issues
- Fixes issues with VACUUM, GIN indexes, and hash indexes that could lead to data corruption
- Fixes for using parallel queries and logical replication.
Read the detailed release document on the official website.
2. Bokeh 0.12.14 released
The Bokeh organization announced the incremental release of Bokeh 0.12.14. This version has two highlights:
- New multi-gesture tools for editing glyphs directly
- Update for compatibility with upcoming Tornado 5.0
Additionally, this release also includes some bug fixes and documentation improvements.
You can visit the Change log on GitHub and the official documentation for a detailed hold on this release.
3. MapR simplifies end-to-end workflow for Data Scientists with MapR Expansion Pack (MEP) 4.1
MapR Technologies announced the availability of MapR Expansion Pack (MEP) 4.1, which allows data scientists and engineers to build scalable deep learning pipelines, instant availability of operational data for data science. It also enables them to achieve over 2X improvement in performance across a variety of data discovery and ad-hoc queries. The MEP 4.1 allows building real-time pipelines and brings data science capabilities to a broad set of users with new languages support.
The team also added features to MapR-DB, MapR Data Science Refinery, and Apache Drill 1.12 in the MapR Expansion Pack 4.1, which include:
- MapR Data Science Refinery extends support for distributing Python archives for PySpark. This allows data scientists to leverage popular Python data science libraries in a distributed way to create scalable deep learning pipelines.
- MapR Data Science Refinery enables Apache Zeppelin to easily leverage a diverse set of Python libraries and environments that can be shared and stored in MapR-XD.
- PySpark jobs can directly read and write to MapR-DB OJAI, making operational data instantly available for data science.
- Python and Java Bindings for MapR-DB OJAI Connector for Apache Spark enable developers to read/write to MapR-DB from Spark using Java and Python. With this, developers can now build data-intensive business applications in Java and Python.
- A new version of Apache Drill, Drill 1.12 enables fast data exploration on operational data in MapR-DB and historical data in Parquet for data scientists, with over 2X performance improvements across a variety of data discovery and ad-hoc queries.
4. Cloudera Altus Analytic DB Beta Available
Cloudera announced the beta version of its Altus Analytic DB, which is built on the Cloudera Altus platform-as-a-service foundation. The Altus Analytic DB also supports the Altus Data Engineering service.
Cloudera’s Altus Analytic DB:
- Allows maintaining a single shared repository of Data in Open File Formats
- Provides multiple clusters over shared data
- Provides a fully controlled data security
- Makes it easy to provision a cluster
Read more about each feature in detail on Cloudera’s official website.
5. Extract! 4.0 – the first fully Deep learning powered Resume Parsing Solution
Textkernel announced the first Deep learning powered ‘Resume Parsing Solution’ named Extract! 4.0.
The resume parsing software is currently available in the English language. Matt McNair, VP Global Services at CareerBuilder – Textkernel’s parent company, said, “Deep Learning has transformed entire industries including automotive, healthcare, retail and financial services. Today, Textkernel is revolutionizing the HR domain with its launch of Extract! 4.0.”
To have a detailed information on Extract! 4.0 and Deep Learning, visit Textkernel’s official website.