Big Data AnalyticsOver the last few years, organizations across public and private sectors have made a strategic decision to turn big data into competitive advantage. The challenge of extracting value from big data is similar in many ways to the age-old problem of distilling business intelligence from transactional data. At the heart of this challenge is the process used to extract data from multiple sources, transform it to fit your analytical needs, and load it into a data warehouse for subsequent analysis, a process known as “Extract, Transform & Load” (ETL). The nature of big data requires that the infrastructure for this process can scale cost-effectively. Apache Hadoop has emerged as the de facto standard for managing big data. This whitepaper examines some of the platform hardware and software considerations in using Hadoop for ETL.
EDW & Big Data Interconnect
Apache HadoopApache Hadoop is an open source distributed software platform for storing and processing data. Written in Java, it runs on a cluster of industry-standard servers configured with direct-attached storage. Using Hadoop, you can store petabytes of data reliably on tens of thousands of servers while scaling performance cost-effectively by merely adding inexpensive nodes to the cluster.
Map Reduceare programming languages that simplify development of applications employing the MapReduce framework. HiveQL is a dialect of SQL and supports a subset of the syntax. Although slow, Hive is being actively enhanced by the developer community to enable low-latency queries on Apache HBase and HDFS. Pig Latin is a procedural programming language that provides high-level abstractions for MapReduce. You can extend it with User Defined Functions written in Java, Python, and other languages.
Apache HiveApache Hive are programming languages that simplify development of applications employing the MapReduce framework. HiveQL is a dialect of SQL and supports a subset of the syntax. Although slow, Hive is being actively enhanced by the developer community to enable low-latency queries on Apache HBase and HDFS. Pig Latin is a procedural programming language that provides high-level abstractions for MapReduce. You can extend it with User Defined Functions written in Java, Python, and other languages.
Apache FlumeApache Flume is a distributed system for collecting, aggregating, and moving large amounts of data from multiple sources into HDFS or another central data store. Enterprises typically collect log files on application servers or other systems and archive the log files in order to comply with regulations. Being able to ingest and analyze that unstructured or semi-structured data in Hadoop can turn this passive resource into a valuable asset.
Investigation of Information Needs
It is well known that the volume, velocity and variety of data are expanding at an exponential rate.
Organizations that learn how to harness and integrate data into their business stand to gain a competitive advantage.
Know how big data can generate value for you Make sure that you can capture and analyse big data Make use of the insights that big data provides.
- Business and use case definition and validation
- Solution architecture and design
- Operating model definition and deployment
- Technical reference architecture
- End-to-end solution development from build through support
- Integration with transaction systems
- Evaluation and selection
- Sizing, installation and configuration
- Maintenance and support
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Data Lake makes information stored in structurally and spatially heterogeneous data sources with complex storage modes reliably accessible at any time to help support your optimal business decisions. A Data Lake is practically synonymous with a modern data warehouse. As end users are faced with larger and more complex challenges set by new innovations and the progress of technology, which in turn impose new demands on data storage systems, making the evolution of data processing and storage an inevitable next step in keeping up with such developments. This paradigm shift has resulted in a new and conceptually different approach to data storage – storing all types of data in a single location regardless of size and complexity, using increased computing power with massive parallelisation and distributed processing and the ability to process large amounts of data in a negligible amount time and with minimal load to current systems.
While the standard data warehouse model traditionally stores data in a hierarchical structure, Data Lake architecture assigns each data element a unique identifier that contains extended metadata tags associated with the corresponding element. When required by business operating procedures, analysis can be performed at any time on relevant data groups stored within a data centre of Data Lake which transforms such data into potentially useful and applicable user information.
Our consultants are ready to support you with their technical and professional know-how and expertise to successfully implement Data Lake solutions and create a modern data warehouse, allowing you to retrieve, process, and convert data into indispensible information.
Easier data access to data across the organization
Access structured and unstructured data residing both on premises and in the cloud.
Faster data preparation
Take less time to access and locate data, thereby speeding up data preparation and reuse efforts
Components of the data lake can be employed as a sandbox that enables users to build and test analytics models with greater agility.
More accurate insights, stronger decisions
Track data lineage to help ensure data is trustworthy.
Manage large volumes and different types of data with open source Hadoop. Tap into unmatched performance, simplicity and standards compliance to use all data, regardless of where it resides. Visualize, filter and analyze large data sets into consumable, business-specific contexts.
Build algorithms quickly, iterate faster and put analytics into action with Spark. Easily create models that capture insight from complex data, and apply that insight in time to drive outcomes. Access all data, build analytic models quickly, iterate fast in a unified programming model and deploy those analytics anywhere.
Stream computing enables organizations to process data streams which are always on and never ceasing. This helps them spot opportunities and risks across all data in time to effect change.
Governance and metadata tools enable you to locate and retrieve information about data objects as well as their meaning, physical location, characteristics, and usage.