Data Virtualization with Big Data Organizations across the globe today are at a constant pressure of managing the three Vs of Big Data, namely volume, velocity, and value.
Managing data in silos and ensuring data security is a serious concern. Sustainable development and need for a conducive environment to ensure security and accurate predictive analysis have become the call of the day. Companies are looking for cloud-based data storage and management solutions that do not come with a legacy tag. Here we discuss the threats and challenges of storage and maintenance and the Data virtualization solutions in Big Data. Data Challenges and Big Data: Data today flows in various forms; unstructured data coming from unwarranted sources (such as Twitter data feeds, online conversations, click streams, and network traffic) or structured data (such as summarization pages, lineage, auditability, and privacy policies) which come from known sources and voluminous. Organizations also deal with semi-structured data from text messages, audio, and video clips. Semi-structured data, though less voluminous and flowing mostly from known sources require additional processing. Data Virtualization Benefits: Data virtualization through Big Data has a three-way benefit: storage, security, and availability.
Data which comes in huge volume (often a few terabytes for some companies) and from various sources is securely stored for future use. Big Data takes care of the Hadoop data, coming from unknown sources, converts them into meaningful information, which is both relevant and valuable. Most of the humongous data pass through a single data access layer and are delivered as integrated data services to users and applications in real-time or near real-time. Availability, the third and most important tenet of Data virtualization with Big Data ensures that processed data is well integrated with other systems, and therefore available for harnessing Big Data for analytics and operations, in near future. In addition, data virtualization allows companies to access data source types from both formal and informal sources, by integrating them with traditional relational databases, for predictive analysis and more informed decision making. Killer Tools for Data Virtualization: Big Data makes use of open source technologies such as Hadoop, Amazon S3, and Google Big Query to integrate distributed data clusters and makes them available across organizations. · Hadoop:Most organization are cashing in on Hadoop today for data integration. Hadoop operates like an autonomous operating system that is composed of a conglomeration of software residing in a distributed data environment.
Hadoop comprehensive analysis of multiple variables and data sets, thereby helping organizations make informed decisions. Its ability to process large sets of disparate data gives Hadoop users gain competitive advantage, getting an all-inclusive customers, operations, opportunities, and take steps for risk analysis. Its ability to analyze huge data sets and cost-effective solutions, make Hadoop a preferred partner for data virtualization in most organizations. It offers a better and more reliable storage solution than traditional data management systems, at a much lower price. · Amazon S3:Amazon Simple Storage Service, more commonly known as Amazon S3 offers cloud-based secure and reliable storage service to developers and IT teams.
It stores data as decomposed objects within configurable resources called “buckets”. Amazon S3 can save huge amounts of data within a single bucket that can also be retrieved, updated, and deleted. Amazon offers a flexible rent-by-the-hour licensing. You may choose from a wide range of pricing options, when buying the Denodo Platform for AWS, including the number of data sources, the volume of concurrent queries, or the number of results returned.
· Google BigQuery:Google BigQuery, another well-known tool for data virtualization is a cloud-based enterprise data warehouse that allows storage and retrieval of humongous datasets. In addition, BigQuery provides a web UI and a command line tool, and several access methods such as a REST API and multiple client libraries (Java, .NET or Python). Google BigQuery offers seamless data deployment, maintenance, and upgrade of the customer database. It makes use of multi-tenant services driven by low-level Google infrastructure technologies such as Dremel, Colossus, Jupiter, and Borg to run queries at a remarkable speed. BigQuery is also much simpler to use.
You may simply start by loading data and run SQL commands. It does not require VMs or additional storage or hardware resources to work on. BigQuery also does not require you to setup disks, define replication, configure compression and encryption, to upgrade data sets. What’s more? Data may be uploaded in CSV or JSON format. You can upload up to 500 files with a job size of 1 terabyte at a time. You may use Google Cloud Storage for importing information.
The year 2018 will see newer tools and apps for data virtualization. Some of the hot tools that are already making mark include Tableau, infogram, ChartBlocks, datawrapper, Plotly, RAW, and Visual.ly. Visual.ly, a visual content service provider has a dedicated big data visualization service.
It has an enviable client base that includes top global brands like VISA, The Huffington Post, Ford and The National Geographic. At Datawrapper, clients can upload their data, create and publish a chart or map. Here, you can also create customized layouts that require zero coding. Keep watching this space for more updates on Big Data and Data Virtualization.