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DataVision Home. ApatarForge - Apatar Open Source Data Integration Community. JMagallanes. Welcome to Magallanes.


The open source project JMagallanes is an end user application for Olap and Dynamic Reports written in Java/J2EE. It combines static reports (based on JasperReports), a Swing pivot table for OLAP analysis, and charts (based on JFreeChart). It reads from many data sources as SQL, Excel, XML, and others, and produces many outputs as PDF, XML, and application specific files for later off-line visualization of reports. Watch the demo video here.

Take advantage of the introductory offer of JMagallanes Products! Complete the new survey! With this initial deploy it is possible to use all the features offered by the tool, also making your own reports. J2EE support, with an EJB based release, and its corresponding client tool. To know more about the functionality, go to Features. If you want to see organizations that successfully implement JMagallanes, enter here. Talend. Sparklines for Excel® Open Studio: Improve Data Integration. Talend Open Studio for Data Integration helps you get your data to the right place, in the right form, at the right time.

Open Studio: Improve Data Integration

The leading open source ETL solution for data warehousing and business intelligence, Talend Open Studio for Data Integration is: Powerful and versatile - Transform, move, and synchronize data across all your heterogeneous sources and targets.Easy to use - Start productive work right away with an intuitive interface rich in modeling tools, job-building components, and more than 450 data connectors, including the Cloud.Proven in the field - Hundreds of thousands of users manage their critical data with Talend Open Studio for Data Integration, from SMBs to some of the largest corporations in the world.Ready to start today - Talend Open Studio for Data Integration is free to download and use, for as long as you want.

No budget battles or endless delays - just faster, easier data integration, starting today. CloverETL Designer Overview. Visualize data flows Place a sequence of components into the transformation graph and connect them with edges that channel data from one component to another.

CloverETL Designer Overview

Variety of flexible components Choose from a variety of highly configurable components to build your transformations. Manage assets Easily manage your assests like input data, shared metadata, connections, scripts, Java sources in CloverETL projects either in your local workspace or remotely on the CloverETL Server. Instantly edit, generate, and preview metadata Metadata in CloverETL describes how data looks. Shared metadata is a great tool to keep large projects clean and organized. Built-in debugging You can define debugging edges during development and then watch and filter real data coming through the edges almost in real time. Visual data mapping For simple mappings use the drag & drop transform dialog, which let's you even define simple expressions without any coding. Drag&drop interface CTLBusiness logic language. BIRT Home. Jasperreports : JasperForge.

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File Reader and variable. Reading multiple files. Writing multiple files. Predictive Analytics: When Big Data and Predictive Analytics Collide. Big Data is usually defined in terms of Volume, Variety and Velocity (the so called 3 Vs).

Predictive Analytics: When Big Data and Predictive Analytics Collide

Volume implies breadth and depth, while variety is simply the nature of the beast: on-line transactions, tweets, text, video, sound, ... Velocity, on the other hand, implies that data is being produced amazingly fast (according to IBM, 90% of the data that exists today was generated in the last 2 years), but that it also gets old pretty fast. In fact, a few data varieties tend to age quicker than others. To be able to tackle Big Data, systems and platforms need to be robust, scalable, and agile. It is in this context that IntelliFest 2012 came to be. Dr. Abstract: Predictive analytics has been used for many years to learn patterns from historical data to literally predict the future. Big data involves large amounts of structured and unstructured data that are captured from people (e.g., on-line transactions, tweets, ... ) as well as sensors (e.g., GPS signals in mobile devices).

Automate reading csv files.