In May 2014, ZeroTurnaround, a software company who created JRebel & LiveRebel, released the result of their global survey on 2164 Java professionals for their 5th year in Java development research reports.
The following are the results for The 2014 Leaderboard of Java Tools and Technologies survey, which has a 2.1% sample error:
- 82.5% - JUnit : Top testing framework used by developers
70% - Jenkins : Most used CI Server in the industry
69% - Git : #1 Version Control technology out there
67% - Hibernate : the top ORM Framework used
65% - Java 7 : the industry leader for SE Development
64% - Maven : Most used build tool in Java
64% - Nexus : the main repository used by developers
56% - MongoDB : The NoSQL technology of choice
55% - FindBugs : most-used Static Code Analysis Tool
50% - Tomcat : the most popular Application Server
49% - Java EE 6 : found in the most enterprises
48% - Eclipse : the IDE used more than any other
40% - Spring MVC : most commonly used Web Framework
32% - MySQL : the most popular SQL technology
The top four technologies in which developers are really interested in are as follows:
- 58% - Gradle : almost 6 in 10 developers say they want to learn more about build tool.
49% - IntelliJ IDEA : almost half of developers would rather use IntelliJ than any other IDE
47% - Scala : 47% choose Scala as their next JVM language
35% - Java 8 : over 1/3 of developers see getting familiar with Java 8 as their highest priority until 2015
Seventy one percent of the Software Developers (which Java professionals commonly called as) build Web Apps while only 3% voted for creating apps for Mobile. Most of them still use Microsoft Windows (50%) for desktops and Google Android (66%) for mobiles.
Spring announced Dijkstra's , a Spring Data release train, General Availability release in May 20, 2014 in a blog written by Oliver Gierke, Spring Data’s Project Lead.
Dijkstra is named after Edsger Wybe Dijkstra, a Dutch computer scientist, who is known for his contributions to computer science: shortest path algorithm also known as Dijkstra's algorithm, the Shunting Yard algorithm, the MultiProgramming System, Banker's algorithm, and the Semaphore.
He said that the release train includes the following five new modules:
- Spring Data Elasticsearch – it is led by Mohsin Husen. With Elasticsearch it is now easy to build applications that use new data access technologies and also offer improved support for relational database technologies. Spring Data Cassandra – led by David and Matthew Adams. The Apache Cassandra NoSQL Database provides new capabilities and has a similar interface to previous Spring Data modules. Both of these modules also held their first GA recently.
- Spring Data Gemfire - Pivotal Gemfire is a distributed data management platform used to easily create highly scalable Spring-powered applications.
- Couch base - aims to offer Spring-based programming model for new datastores while keeping store-specific features and capabilities.
Amazon Redshift is described as a “fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools” which was initially released last February 2014. Launching data warehouse is now easier using Redshift, since it takes charge of the infrastructure which allows you to pay more attention to data and analytics.
In May 8, 2014, Amazon released a blog about the additional support for the twelve new features of Redshift which are as follows:
- JSON Support
Data in JSON format can now be loaded directly to Redshift without preprocessing. You can now specify the mapping of JSON elements to Redshift column names in a jsonpaths file using this new option.
- Copy from Elastic MapReduce
Copying of data from Elastic MapReduce cluster to a Redshift cluster is now possible using the COPY command. You can copy the following formats: fixed-width files, character-delimited files, CSV files, or JSON-formatted files.
- Unload to a Single File
- Increased Concurrency
Maximum of 50 queries can now be configured simultaneously across all of your queues. The increase in the level of concurrency will let you increase your query performance on some workloads.
- Max Result Set Size
Cursor counts and result set sizes can now be configured, however, you should note that memory consumption will also increased if you use larger values.
- Regular Expression Extraction