There are limitless opportunities to harness information of all types – to discover patterns, make connections, and optimize business outcomes in ways that were previously unimaginable.
However, in order to realize this potential, it is vital to have an information foundation that can integrate and manage all sorts of information and turn it into a trusted resource for business decision-making.
It is critical for all people in the organization to make decisions based on the best data available. However, that data is often spread across multiple systems and applications, adding complexity and uncertainty to many business processes.
For that reason, creating a trusted information foundation is critical. The Hadoop ecosystem can assist.
Information integration and governance is about taking a proactive approach to not just fix data quality, but to ensure the data remains of high quality on an ongoing basis, protecting all enterprise data assets and mastering data through its lifecycle. All of this while continuing to deal with cost pressures and ensure audit and compliance mandates are met.
Recent studies confirm that businesses who lead in the adoption of analytics have 33% more revenue growth and 12 times more profit growth than laggards – and that gap is widening.
Everyday, we create 2.5 quintillion bytes of data - 90% of the data in the world today has been created in the last two years alone.
Thanks to technology advancements, we can now gain insight from sources such as social media, call data records, clickstreams and emails at a reasonable cost, to better understand customer needs, identify fraud before it happens and to optimize all sorts of business decisions.
Organizations often fail to consider that the more data in a database, the slower an application will perform. Purging databases of the old data allows room for the new.
By archiving data, you can make your applications more efficient, while retaining data that doesn’t need to reside in the production database system.
Moving data into tier 2 or 3 storage speeds application performance and provides a better experience to your users.
It is crucial to understand the different concepts of data lifecycle governance including test data management, database archiving, data deduplication and data retention management and how organizations around the world are making their applications more efficient overall through the automation of these capabilities.
Standard in many backup and archival products, data reduction is now becoming more prevalent for primary storage. The main drivers for this phenomenon are measurable cost savings from having to buy fewer disks and reducing annual support fees, to lowering operational expenses related to storage management.
Data reduction may also have a pleasant impact on data storage performance: by not having inactive data occupy valuable high-performance storage, overall storage and application performance may get a welcome performance boost.
These are the main data reduction techniques that are being applied on primary storage systems:
• Choosing the right RAID level
• Thin provisioning
• Efficient clones
• Automated storage tiering
See: Data Storage Technologies
See also: Primary Storage Data Reduction
Master data management (MDM) is fundamental to achieving objectives and accurate decision making. However, without a complete strategy in place it can be difficult to meet these objectives.
MDM comprises a set of processes and tools that defines and manages data. MDM lies at the core of many organizations’ operations, and the quality of that data shapes decision making. MDM helps leverage trusted business information - helping to increase profitability and reduce risk.
Master data is reference data about an organization’s core business entitles. These entities include people (customers, employees, suppliers), things (products, assets, ledgers), and places (countries, cities, locations). The applications and technologies used to create and maintain master data are part of a master data management (MDM) system.
Quality is critical to ensuring that the most accurate data is consistently available to the organization at all times and information integration and governance policies, processes, and technologies can help.
It is critical to implement practical information governance strategies and technologies while gaining a unified, complete, consistent, standardized and secure view of your data to drive critical business decisions.
Learn more about how MDM ties into information governance and how better governance enables more successful MDM.
At Rose we can show you how best to leverage innovative techniques and learn how to:
Enhance the quality, availability and integrity of your data
Align information and related projects to business goals
Plan, understand and optimize business performance
Cut costs while integrating, managing and protecting information
Enforce data quality standards and stewardship policies
Manage regulatory compliance demands
Optimize and improve critical decision making
Leverage support for master data management (MDM) programs
Learn how to transform your organization's data into a trusted, strategic asset with a winning information governance program that enables you to proactively manage information over its lifetime.
Data Warehouse / Business Intelligence (DW/BI)
Game-changing Effects of Big Data
Hadoop is not a single technology - it is an ecosystem that includes many technologies. The Hadoop stack includes more than a dozen components, or subprojects:
Hadoop. Java software framework to support data-intensive distributed applications
ZooKeeper. A highly reliable distributed coordination system
MapReduce. A flexible parallel data processing framework for large data sets
HDFS. Hadoop Distributed File System
Oozie. A MapReduce job scheduler
HBase. Key-value database
Hive. A high-level language built on top of MapReduce for analyzing large data sets
Pig. Enables the analysis of large data sets using Pig Latin. Pig Latinis a high-level language compiled into MapReduce for parallel data processing.
Hadoop is great for:
• Applications that boil lots of data down into ordered or aggregated results – sorting, word and phrase counts, building inverted indices mapping phrases to documents, phrase searching among large document corpuses.
• Batch analyses fast enough to satisfy the needs of operational and reporting applications, such as web traffic statistics or product recommendation analysis.
• Iterative analysis using data mining and machine learning algorithms, such as association rule analysis or k-means clustering, link analysis, classification, Naïve Bayes analysis.
• Statistical analysis and reduction, such as web log analysis, or data profiling
• Behavioral analyses such as click stream analysis, discovering content-distribution networks, viewing behavior of video audiences.
• Transformations and enhancements, such as auto-tagging social media, ETL processing, data standardization.
Don't forget MapReduce
MapReduce is a programming model introduced and described by researchers at Google for parallel computation involving large data sets that are distributed across clusters of many processers. In contrast to the explicitly parallel programming models typically used with imperative language such as Java and C++, the MapReduce programming model is reminiscent of functional languages such as Lisp and APL, in its reliance on two basic operational steps:
• Map which describes the computation or analysis to be applied to a set of input key/value pairs to produce a set of intermediate key/value pairs, and
• Reduce, in which the set of values associated with the intermediate key/value pairs output by the Map operation are combined to provide the results.
Conceptually, the computations applied during the Map phase to each input key/value pair are inherently independent, which means that both the data and the computations can be distributed across multiple storage and processing units and automatically parallelized.
Hadoop changes the economics and the dynamics of large scale computing. Hadoop enables a computing solution that is:
Scalable– New nodes can be added as needed, and added without needing to change data formats, how data is loaded, how jobs are written, or the applications on top.
Cost effective– Hadoop brings massively parallel computing to commodity servers. The result is a sizeable decrease in the cost per terabyte of storage, which in turn makes it affordable to model all your data.
Flexible– Hadoop is schema-less, and can absorb any type of data, structured or not, from any number of sources. Data from multiple sources can be joined and aggregated in arbitrary ways enabling deeper analyses than any one system can provide.
Fault tolerant– When you lose a node, the system redirects work to another location of the data and continues processing without missing a beat.
Downloading and installing Hadoop
Hadoop Related Downloads
Oozie – Yahoo!'s workflow engine for Hadoop
Download the source code of Oozie, Yahoo!'s workflow engine for Hadoop:
Apache Hadoop Sandbox
Download the sandbox version of Apache Hadoop with security and Pig. The sandbox version contains a VMWare(TM) based virtual machine with a preinstalled Hadoop cluster, enabling easy setup and experimentation.
Apache Hadoop is an open source software project that enables the distributed processing of large data sets across clusters of commodity servers. It is designed to scale up from a single server to thousands of machines, with a very high degree of fault tolerance.
Rather than relying on high-end hardware, the resiliency of these clusters comes from the software’s ability to detect and handle failures at the application layer.
Apache Hadoop has two main subprojects:
MapReduce - The framework that understands and assigns work to the nodes in a cluster.
HDFS - A file system that spans all the nodes in a Hadoop cluster for data storage. It links together the file systems on many local nodes to make them into one big file system. HDFS assumes nodes will fail, so it achieves reliability by replicating data across multiple nodes.
Hadoop is supplemented by an ecosystem of Apache projects, such as Pig, Hive and Zookeeper, that extend the value of Hadoop and improves its usability.
The Cube - Hadoop Summit 2012 - Day One Wrap-Up
The Cube - Hadoop Summit 2012 - Abhi Mehta, Day 1
The Cube - Hadoop Summit 2012 - Abhi Mehta, Day 2, Part 1
Apache Hadoop (hadoop) on Twitter
Apache Pig Releases
Here are some MapReduce-ish implementations, all of which are either coupled to a single storage system, a single programming language, or implement only a small subset of the features of a mature MapReduce implementation:
Cloud MapReduce: http://code.google.com/p/cloudma...
Galago's TupleFlow: http://www.galagosearch.org/guid...
Plasma MapReduce: http://projects.camlcity.org/pro...
Elastic Phoenix: https://github.com/adamwg/elasti...
Also, Microsoft's DryadLINQ is available under an academic license (not quite open source)
MongoDB (from "humongous") is a scalable, high-performance, open source NoSQL database. Written in C++
* Parallel processing for the rest of us
* Write your scripts in Ruby
* Works with Amazon EC2 and S3
* split -> process -> merge
* As easy as `gem install cloud-crowd`
* Generating or resizing images.
* Encoding video.
* Running text extraction or OCR on PDFs.
* Migrating a large file set or database.
* Web scraping.
~ Documentation ~
Spark is an open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write.
To run programs faster, Spark provides primitives for in-memory cluster computing: your job can load data into memory and query it repeatedly much quicker than with disk-based systems like Hadoop MapReduce.
To make programming faster, Spark integrates into the Scala language, letting you manipulate distributed datasets like local collections. You can also use Spark interactively to query big data from the Scala interpreter.
Stratosphere is a DFG-funded research project and investigates "Information Management on the Cloud". The Stratosphere System consists of:
PACT Programming Model
Nephele Execution Engine
The system is highlighted by the following features:
Easy definition and massively parallel execution of complex data analysis tasks.
PACT is a generalization and extension of the well-known MapReduce programming model.
A cost-based optimizer compiles PACT programs to Nephele dataflow graphs.
Nephele executes dataflow graphs in a massively parallel and very flexible fashion.
Go to: https://www.stratosphere.eu/
Prior to usage, you need to register at the mailing list:
Dr. Volker Markl
Raum EN 728
+49 30 314 23555
+49 30 314 21601
CouchDB is a noSQL database that uses JSON for documents,
Unlike in a relational database, CouchDB does not store data and relationships in tables. Instead, each database is a collection of independent documents. Each document maintains its own data and self-contained schema. An application may access multiple databases, such as one stored on a user's mobile phone and another on a server. Document metadata contains revision information, making it possible to merge any differences that may have occurred while the databases were disconnected.
CouchDB implements a form of Multi-Version Concurrency Control (MVCC) in order to avoid the need to lock the database file during writes. Conflicts are left to the application to resolve. Resolving a conflict generally involves first merging data into one of the documents, then deleting the stale one.
Other features are ACID semantics with eventual consistency, MapReduce, incremental replication and fault-tolerance.
Administration is supported with a built-in web application called Futon.
Gearman provides a generic application framework to farm out work to other machines or processes that are better suited to do the work. It allows you to do work in parallel, to load balance processing, and to call functions between languages.
It can be used in a variety of applications, from high-availability web sites to the transport of database replication events. In other words, it is the nervous system for how distributed processing communicates. A few strong points about Gearman:
Open Source - It's free! (in both meanings of the word) Gearman has an active open source community that is easy to get involved with if you need help or want to contribute.
Multi-language - There are interfaces for a number of languages, and this list is growing. You also have the option to write heterogeneous applications with clients submitting work in one language and workers performing that work in another.
Flexible - You are not tied to any specific design pattern. You can quickly put together distributed applications using any model you choose, one of those options being Map/Reduce.
Fast - Gearman has a simple protocol and interface with a new optimized server in C to minimize your application overhead.
Embeddable - Since Gearman is fast and lightweight, it is great for applications of all sizes. It is also easy to introduce into existing applications with minimal overhead.
No single point of failure - Gearman can not only help scale systems, but can do it in a fault tolerant way.
A Comparison of Approaches to Large-Scale Data Analysis
InfiniDB is an open source, scale-up analytics database engine for your data warehousing, business intelligence and read-intensive application needs.
Enabled via MySQL and purpose-built for an analytical workload with column-oriented technology at its core, the multi-threaded capabilities of InfiniDB fully encompass query, transactional support and bulk load operations.
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HPCC (High Performance Computing Cluster) is a massive parallel-processing computing platform that solves Big Data problems. The platform is open source.
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