Big Data Analytics
What is Big Data Analytics?
The definition of big data holds the key to understanding big data analysis. Like conventional analytics and business intelligence solutions, big data mining and analytics helps uncover hidden patterns, unknown correlations, and other useful business information. 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.
Volume refers to the total amount of data. Many factors can contribute to high volume: 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.
Variety refers to the number of types of data. 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.
Velocity refers to the speed of data processing. The pace at which data streams in from sources such as mobile devices, clickstreams, 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.
Benefits of Big Data Analytics Tools
The main business advantages of big data generally fall into one of three categories: cost savings, competitive advantage, or new business opportunities.
COST SAVINGS: 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.
COMPETITIVE ADVANTAGE: 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 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. Big data insights help organizations boost sales and marketing results, uncover new revenue opportunity, improve customer service, optimize operational efficiency, reduce risk, and improve security.
Big Data Tools Overview
Apache Hadoop
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.
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Apache Spark
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 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.
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Apache Hive
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
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, and HBase make them a popular option for big data analytics.
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.
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Big Data in the Cloud
Big data analytics can be a complex concept, one that many businesses may feel like they’re not ready for. Big data infrastructure can get to be complicated, and without the right personnel on hand, maintaining it can be a monumental task. One solution to this significant problem is for companies to head to the cloud for their big data needs. Many cloud vendors already provide a variety of services through the cloud, and big data analytics is just the latest example of this.
Taking big data to the cloud offers up a number of advantages, including improved 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. Qubole’s big data in the cloud services work on your existing cloud infrastructure, including Amazon Web Services, Microsoft Azure and Google Cloud Platform.
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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.
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 Qubole Supports Big Data Analytics
Many different types of solutions are required to support the wide range of big data use cases. From simple spreadsheets to advanced analytics and marketing solutions to analytics engines, Qubole provides effortless integration to centrally analyze your data all in one spot.
SPREADSHEETS AND ANALYTICS TOOLS: Through ODBC connectors, Qubole customers can connect to Microsoft Excel and tools from leading analytics vendors such as Tableau, Looker, Qlik, Microstratgey, and TIBCO Jaspersoft. In addition, the R statistical programming language can be integrated with Qubole using ODBC/REST APIs.
ANALYTICS ENGINES: Qubole 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: Qubole 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.
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