BIG Data is a collection of large and complex data sets arises out of Digitization of modern world. The combination of structured & un-structured data and data sets in terra bytes/ Penta bytes is making it difficult for normal database management tools and traditional data processing applications to process the data.
For a layman BIG DATA is defined as complexity hidden in the data of your customers, partners, employees, competitors, own data, social media data etc. BIG data services are like bringing simplicity to this complex data by unveiling the right context to take correct business decisions. In even simpler terminology “if you know the past completely and understand the trends than you know the future as well”. Modern day analytics is moving towards giving reports like “WHAT is the best that can happen”. Predictive Analytics will be part of all BI solutions.
Definitely BIG DATA is a Game Changer. It will change how business will happen in the years to come and it will not be everybody’s cup of tea especially those who have not invested on Data Discipline/Business Intelligence & Analytics.
The best news is BDI can do that for you, from trapping your small metric data to give you Big Data Analytics. We can give either using our own tools & open source technology with data complexity managed through Hadoop clusters/nodes OR if you have invested on SAP HANA than we can take care of your entire BIG Data Landscape.
We have internally worked on a HTML5 framework which can give you big data reports on any device – PC/Tablet/Mobile.
We can give you step by step approach to make you Big Data ready. Being BI implementers for more than 10 yrs we know what your data infrastructure and what Analytics process you are lacking to be made ready for BIG DATA Analytics.
This can help us in understanding the gaps and plan for Big Data in a step by step approach on the basis of approved budgets.
Below tables shows the BIGGEST Challenges for Success in Big Data Analytics.
While BDI can take care of most of these points, but customer needs to address “Making the Proper Business Case & overcoming Employee Resistance. BIG Data reports can virtually make life difficult for Casual employees who don’t want to understand the complexity of business coming due to large set of data.
BIG DATA is achieved through 2 important technology directions and in some cases a combination of these 2 gives the most ideal solution.
- Open Source using HADOOP and Related Technologies :-
BDI Systems has good experience on Hadoop Related Technologies and has done multiple projects.
More than structured information stored neatly in rows and columns, Big Data actually comes in complex, unstructured formats, everything from web sites, social media and email, to videos, presentations, etc. This is a critical distinction, because, in order to extract valuable business intelligence from Big Data, any organization will need to rely on technologies that enable a scalable, accurate, and powerful analysis of these formats.
Apache Hadoop is a framework that allows for the distributed processing of such large data sets across clusters of machines.
Apache Hadoop, at its core, consists of 2 sub-projects – Hadoop MapReduce and Hadoop Distributed File System. Hadoop MapReduce is a programming model and software framework for writing applications that rapidly process vast amounts of data in parallel on large clusters of compute nodes. HDFS is the primary storage system used by Hadoop applications. HDFS creates multiple replicas of data blocks and distributes them on compute nodes throughout a cluster to enable reliable, extremely rapid computations. Other Hadoop-related projects at Apache include Chukwa, Hive, HBase, Mahout, Sqoop and ZooKeeper.
Here is a brief introduction to all these technologies:-
Filesystems that manage the storage across a network of machines are called distributed filesystems. HDFS is designed for storing very large files with write-once-ready-many-times patterns, running on clusters of commodity hardware.
MapReduce is a framework for processing highly distributable problems across huge datasets using a large number of computers (nodes), collectively referred to as a cluster. The framework is inspired by the map and reduce functions commonly used in functional programming.
Chukwa is a Hadoop subproject devoted to large-scale log collection and analysis. Chukwa is built on top of HDFS and MapReduce framework and inherits Hadoop’s scalability and robustness.
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query and analysis. HiveServer provides a Thrift interface and a JDBC / ODBC server.
HBase is the Hadoop application to use when you require real-time read/write random-access to very large datasets. It is a distributed column-oriented database built on top of HDFS.
Mahout is an open source machine learning library from Apache. It’s highly scalable. Mahout aims to be the machine learning tool of choice when the collection of data to be processed is very large, perhaps far too large for a single machine.
Sqoop allows easy import and export of data from structured data stores such as relational databases, enterprise data warehouses, and NoSQL systems. The dataset being transferred is sliced up into different partitions and a map-only job is launched with individual mappers responsible for transferring a slice of this dataset.
ZooKeeper is a distributed, open-source coordination service for distributed applications. It exposes a simple set of primitives that distributed applications can build upon to implement higher level services for synchronization, configuration maintenance, and groups and naming.
SAP HANA Services (native & BW on HANA)
- HANA Architecture & Landscape Planning & Deployment
- HANA Administration
- HANA Performance Tuning
- HANA Use Case Development
- HANA Solution Design
- HANA Solution Development
- HANA Solution Testing & Deployment
- HANA Production Support/ Training
- HANA Ramp Ups (pre-GA)