Big Data is used to refer large volumes of unorganized data stored by companies which needs to be processed, analysed and ultimately used to make better decisions in benefit of the organizations. More recently, the data sets have become so huge and voluminous that it is getting tougher for companies to continue storing and processing these unstructured data.
Almost 70-80% of data captured today is unstructured may it be from climate sensors, social media posts, digital videos/pictures, purchase transactions, GPS signals etc. All these accounts for Big Data most of which has been surprisingly acquired in the past 2 years.
Legacy systems which were previously used for storing and processing data have eventually become obsolete when being considered for Big Data. This is what accounts for commencement of Hadoop for gathering, storing, processing and retrieving petabytes of data.
How does Hadoop help?
- Companies are finding that important predictions can be made by sorting out and analyzing Big Data. Since, most of it is unstructured, it must be formatted in a way that makes it suitable for data mining/research and subsequent analysis.
- Hadoop is a core platform for structuring Big Data for the reason that it solves the difficulty of formatting voluminous data and subsequent analysis purposes.
- It uses a distributed computing architecture comprising of multiple servers using commodity hardware and relatively making it inexpensive to scale and support extremely huge data stores.
HBase is a non-relational (NoSQL) database that runs on top of the Hadoop Distributed File System (HDFS). It provides quick access to large quantities of data and also adds transactional capabilities to Hadoop, allowing users to conduct inserts, deletes and updates. Hadoop is most suited for offline batch-processing while HBase is used in real-time needs for Big Data.
How does Laitkor help?
- Hadoop is a new technology compared to other mature & relational databases, was also not designed deliberately to meet expectations of data security and integrity.
- Laitkor addresses these issues having with in depth expertise in both Hadoop technology and enterprise data security.
- Our enterprise-class data encryption for Big Data, its access-control & authentication solutions are well designed and optimized to suit the specific requirements of Hadoop’s distributed and complex computing architecture.
- It has fault tolerant storage for large amount of data with flexible data model.
- Real-time lookups.
- Strongly consistent and atomic row-level operations for precision.
- Sharding and load balancing of tables automatically.
- Server-side processing via filters and coprocessors with replication across the data center.
Laitkor Big Data Work
We have done data visualizations using R programming language. R is a free programming language and software environment used for statistical computing and graphics best suited for data analysis.
Even though there are a lot of softwares available for data analysis like point & click GUI based systems eg SPSS, spreadsheets, batch oriented & procedure based systems eg SAS, data mining systems etc, we prefer using R for following reasons:
- R is a free language
- Graphics and Data Visualizations
- Built in statistical analysis toolkit
- Powerful, cutting-edge analytics extensions
- Robust, vibrant community resources
How is D3.js helpful?
D3.js is a most powerful web data visualization library available today allowing building interactive graphics and data driven web applications.
Laitkor has used D3.js for building client dashboards.