Big Data Analytics is fast becoming reality and the need for many sectors. The definition of big data holds the key to understanding the big data analysis. According to the Gartner IT Glossary, Big Data is high-volume, high-velocity, and high-variety information assets that demand cost effective, innovative forms of information processing for enhanced insight and decision making. Many factors amounts to need of Big Data analytics, first being high volume of data: sensor and machine-generated data, networks, social media, and much more. Enterprises are awash with terabytes and, increasingly, petabytes of big data. As infrastructure improves along with storage technology, it has become easier for enterprises to store more data than ever before. Second is data being unstructured and unsystematic. Big data extends beyond structured data such as numbers, dates, and strings to include unstructured data such as text, video, audio, click streams, 3D data, and log files. The more sources that data is collected from, the more variety will be found within data assets. Third is the Speed to process huge volume of data. The pace at which data streams in from sources such as mobile devices, click-streams, high-frequency stock trading, and machine-to-machine processes is massive and continuously fast moving. The faster that pace becomes, the more data can be analyzed for discovering new insights.
Like conventional analytics and business intelligence solutions, big data mining and analytics helps uncover hidden patterns, unknown correlations, and other useful business information. However, big data tools can analyze high-volume, high-velocity, and high-variety information assets far better than conventional tools and relational databases that struggle to capture, manage, and process big data within a tolerable elapsed time and at an acceptable total cost of ownership.
Big Data Analytics is here now
Big data and big data tools offer many benefits. The main business advantages of big data generally fall into one of three categories: cost savings, competitive advantage, or new business opportunities.
Big data tools like Hadoop allow businesses to store massive volumes of data at a much cheaper price tag than a traditional database. Companies utilizing big data tools for this benefit typically use Hadoop clusters to augment their current data warehouse, storing long-term data in Hadoop rather than expanding the data warehouse. Data is then moved from Hadoop to the traditional database for production and analysis as needed. Versatile big data tools can also function as multiple tools at once, saving organizations on the cost of needing to purchase more tools for the same tasks.
According to a survey of 540 enterprise decision makers involved in big data purchases by Webopedia’s parent company QuinStreet, about half of all respondents said they were applying big data and analytics to improve customer retention, help with product development, and gain a competitive advantage. One of the major advantages of big data analytics is that it gives businesses access to data that was previously unavailable or difficult to access. With increased access to data sources such as social media streams and clickstream data, businesses can better target their marketing efforts to customers, better predict demand for a certain product, and adapt marketing and advertising messaging in real-time. With these advantages, businesses are able to gain an edge on their competitors and act more quickly and decisively when compared to what rival organizations do. Needless to say, a business that effectively utilizes big data analytics tools will be much better prepared for the future than one that doesn’t understand how important those tools are.
New Business Opportunities
The final benefit of big data analytics tools is the possibility of exploring new business opportunities. Entrepreneurs have taken advantage of big data technology to offer new services in AdTech and MarketingTech. Mature companies can also take advantage of the data they collect to offer add-on services or to create new product segments that offer additional value to their current customers. In addition to those benefits, big data analytics can pinpoint new or potential audiences that have yet to be tapped by the enterprise. Finding whole new customer segments can lead to tremendous new value.
These are just a few of the actionable insights made possible by available big data analytics tools. Whether an organization is looking to boost sales and marketing results, uncover new revenue opportunities, improve customer service, optimize operational efficiency, reduce risk, improve security, or drive other business results, big data insights can help.
Use cases for big data analysis
Big data analytics lends itself well to a large variety of use cases spread across multiple industries. Financial institutions can quickly find that big data analysis is adept at identifying fraud before it becomes widespread, preventing further damage. Governments have turned to big data analytics to increase their security and combat outside cyber threats. The healthcare industry uses big data to improve patient care and discover better ways to manage resources and personnel. Telecommunications companies and others utilize big data analytics to prevent customer churn while also planning the best ways to optimize new and existing wireless networks. Marketers have quite a few ways they can use big data. One involves sentiment analysis, where marketers can collect data on how customers feel about certain products and services by analyzing what consumers post on social media sites like Facebook and Twitter.
The number of use cases are plentiful, and no industry should think that analytics couldn’t be used in some way to improve their businesses. That type of versatility is part of what has made big data so popular. And these are only a few examples of use cases. As companies and other organizations become more familiar with all of the capabilities granted through big data analytics, more use cases will likely be discovered, adding to big data’s overall value. As with any developing technology, the process may take some time, but eventually its widespread use will lead to the discovery of even more benefits and uses.
Top Big Data Tools Overview:
Hadoop is an open source software framework originally developed by Doug Cutting and Mike Cafarella in 2006. It was specifically built to handle very large data sets. Hadoop is made up of two main parts: the Hadoop Distributed File System (HDFS) and MapReduce. HDFS is the storage component of Hadoop. Hadoop stores data by splitting files into large blocks and distributing it across nodes. MapReduce is the processing engine of Hadoop. Hadoop processes data by delivering code to nodes to process in parallel.
Apache Spark is quickly growing as a data analytics tool. It is an open source framework for cluster computing. Spark is frequently used as an alternate to Hadoop’s MapReduce because it it is able to analyze data up to 100 times faster for certain applications. Common use cases for Apache Spark include streaming data, machine learning and interactive analysis.
Apache Hive is a SQL-on-Hadoop data processing engine. Apache Hive excels at batch processing of ETL jobs and SQL queries. Hive utilizes a query language called HiveQL. HiveQL is based on SQL, but does not strictly follow the SQL-92 standard.
NoSQL databases have grown in popularity. These Not Only SQL databases are not bound by traditional schema models allowing them to collect unstructured datasets. The flexibility of NoSQL databases like MongoDB, Cassandra HBase make them a popular option for big data analytics.
Traditional, row-oriented databases are excellent for online transaction processing with high update speeds, but they fall short on query performance as the data volumes grow and as data becomes more unstructured. Column-oriented databases store data with a focus on columns, instead of rows, allowing for huge data compression and very fast query times. The downside to these databases is that they will generally only allow batch updates, having a much slower update time than traditional models.
Schema-less databases, or NoSQL databases
There are several database types that fit into this category, such as key-value stores and document stores, which focus on the storage and retrieval of large volumes of unstructured, semi-structured, or even structured data. They achieve performance gains by doing away with some (or all) of the restrictions traditionally associated with conventional databases, such as read-write consistency, in exchange for scalability and distributed processing.
This is a programming paradigm that allows for massive job execution scalability against thousands of servers or clusters of servers. Any MapReduce implementation consists of two tasks:
- The “Map” task, where an input dataset is converted into a different set of key/value pairs, or tuples;
- The “Reduce” task, where several of the outputs of the “Map” task are combined to form a reduced set of tuples (hence the name).
Hadoop is by far the most popular implementation of MapReduce, being an entirely open source platform for handling Big Data. It is flexible enough to be able to work with multiple data sources, either aggregating multiple sources of data in order to do large scale processing, or even reading data from a database in order to run processor-intensive machine learning jobs. It has several different applications, but one of the top use cases is for large volumes of constantly changing data, such as location-based data from weather or traffic sensors, web-based or social media data, or machine-to-machine transactional data.
Hive is a “SQL-like” bridge that allows conventional BI applications to run queries against a Hadoop cluster. It was developed originally by Facebook, but has been made open source for some time now, and it’s a higher-level abstraction of the Hadoop framework that allows anyone to make queries against data stored in a Hadoop cluster just as if they were manipulating a conventional data store. It amplifies the reach of Hadoop, making it more familiar for BI users.
PIG is another bridge that tries to bring Hadoop closer to the realities of developers and business users, similar to Hive. Unlike Hive, however, PIG consists of a “Perl-like” language that allows for query execution over data stored on a Hadoop cluster, instead of a “SQL-like” language. PIG was developed by Yahoo!, and, just like Hive, has also been made fully open source.
WibiData is a combination of web analytics with Hadoop, being built on top of HBase, which is itself a database layer on top of Hadoop. It allows web sites to better explore and work with their user data, enabling real-time responses to user behavior, such as serving personalized content, recommendations and decisions.Perhaps the greatest limitation of Hadoop is that it is a very low-level implementation of MapReduce, requiring extensive developer knowledge to operate. Between preparing, testing and running jobs, a full cycle can take hours, eliminating the interactivity that users enjoyed with conventional databases.
PLATFORA is a platform that turns user’s queries into Hadoop jobs automatically, thus creating an abstraction layer that anyone can exploit to simplify and organize datasets stored in Hadoop.
As the data volumes grow, so does the need for efficient and effective storage techniques. The main evolutions in this space are related to data compression and storage virtualization.
SkyTree is a high-performance machine learning and data analytics platform focused specifically on handling Big Data. Machine learning, in turn, is an essential part of Big Data, since the massive data volumes make manual exploration, or even conventional automated exploration methods unfeasible or too expensive
Big Data in the cloud
As we can see, most of these technologies are closely associated with the cloud. Most cloud vendors are already offering hosted Hadoop clusters that can be scaled on demand according to their user’s needs. Also, many of the products and platforms mentioned are either entirely cloud-based or have cloud versions themselves.
Big Data and cloud computing go hand-in-hand. Cloud computing enables companies of all sizes to get more value from their data than ever before, by enabling blazing-fast analytics at a fraction of previous costs. This, in turn drives companies to acquire and store even more data, creating more need for processing power and driving a virtuous circle.
What is big data in the cloud?
Taking big data to the cloud offers up a number of advantages. Improvements come in the form of better performance, targeted cloud optimizations, more reliability, and greater value. Big data in the cloud gives businesses the type of organizational scale many are searching for. This allows many users, sometimes in the hundreds, to query data while only being overseen by a single administrator. That means little supervision is required.
Big data in the cloud also allows organizations to scale quickly and easily. This scaling is done according to the customer’s workload. If more clusters are needed, the cloud can give them the extra boost. During times of less activity, everything can be scaled down. This added flexibility is particularly valuable for companies that experience varying peak times. Big data in the cloud also takes advantage of the benefits of cloud infrastructure, whether they be from Amazon Web Services, Microsoft Azure, Google Cloud Platform, or others.
What are data lakes?
Gathering data from various sources is, of course, only one part of the big data analytics process. All that data needs to be stored somewhere, and that repository is often referred to as a data lake. Data lakes are where data is kept in its raw form, before any organizational structure is used and before any analytics are performed. Data lakes don’t use the traditional structure of files or folders but rather use a flat architecture where each element has its own identifier, making it easy to find when queried. Data lakes are a type of object storage that Hadoop uses, making it an effective way to describe where Hadoop-supported platforms pull their data from. One major benefit of having a data lake is the ability to store massive amounts of data. As big data continues to grow, the need for that near limitless storage capability has grown with it. Data lakes also allow for added processing power while also providing the ability to handle numerous jobs at the same time. These are all capabilities that have been increasingly in demand as more enterprises use big data analytics tools.
How NexCen supports big data analytics?
From simple spreadsheets to advanced analytics and marketing solutions to analytics engines, leading self-service big data platform provides effortless integration to centrally analyze your data all in one spot.
Spreadsheets and Analytics Tools: Through ODBC connectors, NexCen customers can connect to Microsoft Excel and tools from leading analytics vendors such as Tableau, Qlik, MicroStrategy, and TIBCO Jaspersoft. In addition, the R statistical programming language can be integrated ODBC/REST APIs.
Analytics Engines: NexCen offers connectors for massively parallel processing databases such as Vertica as well as relational database engines such as Microsoft SQL Server and the MySQL open source database, and NoSQL databases such as MongoDB.
CRM and Online Marketing Solutions: NexCen also connects to leading CRM and online marketing platforms such as Salesforce.com and online marketing and web analytics solutions such as Omniture and Google Analytics.
NexCen ways of Processing Big Data
unified interface for performing the myriad of use cases and workloads that a data driven organization will face ranging from ad hoc analysis, predictive analysis, machine learning, streaming and MapReduce to name a few. Users without software development skills can leverage the QDS workbench through our SmartQuery interface without even knowing how to write a SQL query.
The NexCen Workbench enables data scientists and analysts to drive their ah-hoc workloads through their processing engines of choice using an easy-to-use SQL query composer or SmartQuery builder tool.
Developers can configure their applications to drive various workloads using a number of common language options.
BI & Visualization Systems
Drive QDS workload using industry-leading, ODBC compliant BI & Visualization tools, including: Tableau, Birst, Qlik, Pentaho etc
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