There is a data revolution going on around the world, and data analytics is the shiny new field that has been drawing professionals, leading them to learn about the top data analytics tools and more. If you think the hype about data analytics and Big Data is overstated, check out these statistics:
- According to the International Data Corporation, the big data and analytics market revenues will increase to $203 billion worldwide in 2020, with a compound annual growth rate of 11.7 percent.
- The U.S. Bureau of Labor Statistics reports that the job market for various data analyst disciplines is growing annually at the rate of 13 percent faster than most other occupations.
- Big Data adoption in organizations increased from 17 percent in 2015 to 59 percent in 2018.
- According to the International Institute for Analytics, businesses using data will save $430 billion compared with competitors in 2020, due to the enhancement of productivity.
- According to a report by IBM, there will be 2,720,000 jobs for data professionals by 2020.
So, data analytics is currently the most lucrative path to ride the Big Data wave and if you want to enter this field, you need to know how to select the right data analysis tools once you’re certified. But Big Data analytics tools have proliferated recently, and selecting the right ones to work with can be challenging. Here we list the 10 most efficient data analytics tools to unleash the potential of Big Data and drive businesses toward more informed processes.
1. Cloudera:
Cloudera is the perfect enterprise solution to help businesses manage their Hadoop ecosystem. The Service Monitor and Host Monitor roles in the Cloudera Management Service stockpile time-series data and health data, as well as Impala query and Yet Another Resource Negotiator (YARN) application metadata. They also deliver intricate data security, which is essential for storing any sensitive or personal data.
2. MongoDB:
MongoDB is an efficient data analytics tool responsible for preserving data for mobile apps, product catalogs, real-time personalization, and content management, providing a single view across multiple systems. Here are some of the benefits of MongoDB:
- It reduces operational overhead by up to 95 percent.
- Its new flexible storage architecture includes the WiredTiger storage engine.
- It has a global distribution with up to 50 replica-set members.
- It takes up to 80 percent less storage space due to compression.
3. Vidyard:
Vidyard is a business video hosting and analytics provider. Companies like Ernst & Young have entrusted their video analytics to Vidyard. With a splendid custom video player and real-time analytics, Vidyard is a boon for anyone looking for a custom solution. Some key metrics monitored by Vidyard include views, average attention span, minutes watched and most popular region.
4. KnowledgeREADER:
KnowledgeREADER, from Angoss, is a portion of a wide suite of data analytics tools; it explicitly addresses text analytics related to customer-oriented and marketing applications. It specializes in visual representation, including dashboards for sentiment and text analysis, and also provides a map of the results of association mining to show words that tend to occur together.
Many of its cutting-edge features make use of the embedded Lexalytics text analytics engine, which is widely recognized for its potential. Entity, theme, and topic extraction are sustained, along with decision and strategy trees for profiling, segmenting and predictive modeling.
5. Meltwater Social (Sysomos):
Meltwater Social, formerly Sysomos, is a powerful social media and data analytics tool to provide deep insight into enterprise marketing and user analytics. The ability to use social listening, audience insight, and brand engagement is an important part of any marketing professional’s toolkit. However, Meltwater Social takes the idea of a social media management platform to an entirely new level with a robust and user-friendly analytics powerhouse on the backend. Powered by a learning type of artificial intelligence technology, the analytics are drawn from bits of social user data to provide complete insights that translate to more than marketing.
6. OpenRefine:
OpenRefine is a software that cleans data to prepare it for analysis. What do we mean by that? Well, OpenRefine lets you cluster into cells any values that might be differentiated by either spelling or capitalization of letters but mean the exact same thing. This tool might appear basic, but it helps fight redundancy. A simple data analysis activity such as clustering customer info into one cell based on geographical location might otherwise be difficult, as each customer might spell or write the locality information a different way. OpenRefine can detect similarities to make clustering easy. It contains a number of clustering algorithms and makes quick work of an otherwise messy problem.
7. Qubole:
Qubole simplifies, speeds up and scales big data analytics workloads against data stored in the cloud on AWS, Google or Azure. This tool takes the stress out of infrastructure wrangling. Once the IT policies are in place, any number of data analysts can collaborate and click to query with the power of Hive, Spark, Presto, and others. Qubole is an enterprise-level data processing engine, and its flexibility and accessibility set it apart from the rest.
8. Tableau:
Some of Tableau's crucial benefits come from its advanced language and storage analytics database. It can help you easily translate data into meaningful business metrics. The online and server versions enable an entire team to build and work collectively with the visualization tool. Tableau can connect to local or remote data in many different formats. Additionally, the Tableau engine can access live data for up-to-date visualizations or warehoused data for much more smoothly moving visualizations. Tableau Public's million-row limit provides a thriving platform for personal use, and the free trial is more than enough to explore the tool effectively.
Tableau 10 also has innovative technology for database connections called Query Fusion, which greatly simplifies queries by looking at all of the queries in the user's dashboard.
9. Chartio:
Chartio lets you chain data sources and executes queries in your browser. You can generate potent dashboards in just a few clicks. Chartio’s visual query language lets you collect data from any source without having to know SQL or any other complicated model languages. It also lets you schedule PDF reports to be exported and emailed. The other significant feature about this tool is that in most formats it doesn’t require a data warehouse. This means that you can get up and running at a faster pace and that the cost of implementation is going to be lower and more predictable when compared to other options mentioned above.
10. Blockspring:
Blockspring is a distinctive tool due to the way it harnesses all of the capabilities of services such as If This Then That (IFTTT) and Zapier on popular platforms such as Excel and Google Sheets. You can connect to a wide array of third-party programs merely by writing a Google Sheet formula. You can post Tweets from a spreadsheet, track your followers and connect to AWS, Import.io, Tableau and more. Blockspring lets you create and share private functions, implement custom tags for enhanced search and discovery, and set API tokens for your whole organization at once.
Kickstart your career in Big Data Analytics
Today, it’s not a bad idea to pursue a career in Big Data Analytics. According to the 2018 Harvey Nash/KPMG CIO Survey, 43 percent of CIOs agree that the largest talent shortage today is in the field of big data and analytics. There’s plenty of opportunities, period. The big data analytics tools are out there, it’s just a matter of learning how to use them. Simplilearn’s Big Data Architect Master’s Program provides potential professionals with all the knowledge and skills needed to land a lucrative position in a field that is in dire need of professionals.
