17 Free Big Data Analytics Tools That Will Help You Tame the “Shrew” Called Data
Know about the top-flight big data analytics tools that will assist you in the reporting, analysis, visualization, integration, and development of analytical platforms to harness the power of “big” data.
At present, there are thousands of big data analytics tools available in the market that promise users insights that have never been uncovered before. The truth, however, lies in the fact that most big data analytics tools available today help save time, money, and resources required to unearth insights. With the recent preference for subscription-based pricing and falling prices of terabytes, data has started piling up, and people across the globe are wondering what can be done with this data. Perhaps there are some insights in those log files? Or maybe a bit of statistical analysis will find some nuggets of gold buried in all of that noise? Maybe we can find enough dollars hidden in the couch cushions of these files to give us all a good raise?
The transformation and rising popularity of “big data” is nothing short of extraordinary. At present, big data analytics has started replacing business intelligence, which subsumed “reporting,” which, in turn, put a nicer gloss on “spreadsheets,” which then beat out the old-fashioned “printouts.” Managers who long ago studied printouts are now hiring mathematicians who claim to be specialists in big data analytics tools to help them solve the same old problem: What’s selling and why?
It’s not fair to presume that these buzzwords are just simple replacements of each other. Big data is a more complicated world because of its sheer scale, which is humungous. The information is usually spread out over numerous of servers, and the work of compiling the data is coordinated among them. Up until a couple of years, the task was mostly delegated to the database software, which would use its magical prowess to compile tables, add up the columns before handing off the data to the reporting software which would then paginate it. This was often harder than it sounds. Database programmers can tell you stories about complicated commands or programs that would lock up the database for hours as it tried to generate a report for the boss who wanted his columns just so.
However, the game is so much different now. We at Quantzig, believe that for every decision maker and data analyst, navigating this world of possible tools can be a tricky business – especially when there are so many options available in the market today. But before you start selecting big data analytics tools for your business, there are a couple of questions that you have to ask yourself:
- Which big data analytics tools are an exact match for your skill set?
- Which big data analytics tools are right for your project?
This is where Quantzig comes into the picture as we help companies unearth and pick the best big data analytics tools for your requirement. To help decision makers and data analysts choose the appropriate platforms, we’ve listed out some of our favorite big data analytics tools in the areas of data storage and management, data cleaning, data mining, data analysis, data visualization, data integration, data languages, and data collection.
Big Data Analytics Tools for Data Storage and Management
If you want to work with big data analytics tools, you need to spare some thought into how to store the data that is being generated. This explains a part of how big data got the distinction as “Big”. Traditional systems are unable to store such high volumes of data and lack the infrastructure to run analytical platforms efficiently.
Big Data Analytics Tools: Hadoop
Hadoop is a popular tool for storing racks and racks of data and has become synonymous with big data analytics. Hadoop helps in the storage of vast amounts of data and has enormous processing power, characterized by the ability to handle numerous concurrent tasks. Basically, it is an open-source framework for the distributed storage of large data sets.
This big data analytics tool is definitely not for beginners. Thus, to truly harness its power, the data analyst or scientist should have an extensive knowledge of Java. At a glance, this seems like a lot of effort, but it is certainly worth the effort as most companies today have integrated such big data analytics tools into their processes.
Additional Tools: Hadoop Distributed File System, Hbase, HIVE, Sqoop, Pig
Big Data Analytics Tools: Cloudera
Cloudera is essentially a fancy brand name for Hadoop with the exception of a few additional services. This big data analytics tool can help businesses to build an enterprise data hub to facilitate better access to the data which is being stored. Since it’s similar to Hadoop, it has an open-source element that allows businesses to manage their Hadoop ecosystem.
Such big data analytics tools carry out the arduous work of managing and administering the entire Hadoop ecosystem and offers an additional layer of data security to protect sensitive or personal data.
Price: Cloudera Manager is free to download, but the prices of services vary
Big Data Analytics Tools: MongoDB
MongoDB is a free and open-source cross-platform document-oriented database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemas. Such big data analytics tools are popular among start-ups as it acts as an alternative to relational databases. This analytics tool is excellent for managing data sets that are dynamic and unstructured in nature.
This big data analytics tool is largely used for storing product catalogs, mobile app and real-time personalization data, and content management and applications – delivering a single view across multiple systems. It’s worth mentioning here that such big data analytics tools are not for novices as the data analyst has to know how to query it using programming language.
Price: Free to download, but the prices of services vary
Additional pointers: MongoDB has their own “University” to help users to learn how to use their services and get certifications.
Big Data Analytics Tools: Talend
Talend is a famous big data analytics tools provider that also offers cloud storage, data integration, and enterprise application integration software and services. It is an open-source program that offers a tool called Master Data Management (MDM). Such big data analytics tools combine real-time data, process integration, and applications with embedded data quality and stewardship.
Since it is an open-source platform, Talend is entirely free – making it the tool of choice for many data operators. Additionally, this tool also helps you build and manage your own data management system, which is a tremendously difficult and complex task.
Price: Free to download, but the prices of services vary.
Big Data Analytics Tools for Data Cleaning
Before you jump steps and look for strategic insights, you need to clean the data. Even though it is a very good practice to create a well-structured data set, sometimes it isn’t possible. Our experience in handling large volumes of data has shown that data sets can come in all shapes and sizes (some good and some not that great!), especially when this data is sourced from the Internet.
Big Data Analytics Tools: OpenRefine
OpenRefine, which was formerly known as GoogleRefine, is one of the many open-source big data analytics tools available in the market today. With the help of such big data analytics tools, you can explore large data sets efficiently and effectively, even if the data set is unstructured.
In terms of the software, OpenRefine is extremely user-friendly and a basic knowledge of data cleaning principles is enough to operate this software. The key selling point of big data analytics tools such as OpenRefine is the fact that they have a huge community with lots of contributors – meaning that the software is continually getting better and better.
How to get started: Check out their homepage tutorial.
Big Data Analytics Tools: DataCleaner
The data analysts at DataCleaner know that data manipulation is a long and drawn out task. Most of the data visualization tools today can only read nicely structured, clean data sets. This is where DataCleaner comes into the picture as it does all the heavy lifting for you and transmutes messy semi-structured data sets into clean readable data sets, which can subsequently be interpreted by all of the visualization companies.
It also offers data warehousing and data management services on a free trial basis. The pricing points and subscription models are given here.
Price: Free trials for certain products, but the prices for other services and products vary.
Big Data Analytics Tools for Data Mining
Data mining should never be confused with data extraction, which will be covered later. Data mining is the process of discovering insights within a database, which is very different from extracting insights from web pages and then converting them to structured data sets. The primary aim of data mining is to make accurate predictions and decisions on the data you have at hand.
Big Data Analytics Tools: RapidMiner
RapidMiner is one of the world’s most foremost big data analytics tools for predictive analytics and boasts an impressive client list, including PayPal, Deloitte, eBay, etc. It is powerful, easy to use, and has great community support. A standout feature of this big data analytics tool is the fact that it allows users to integrate their own specialized algorithms through APIs.
Their UI is similar to Yahoo! Pipes, which basically means that users don’t have to be proficient in coding to operate this tool.
Price: Price points vary from $2500/year to $10,000/year.
Big Data Analytics Tools: Oracle Data Mining
Among all the companies that offer big data analytics tools, Oracle is another big hitter. As a part of their advanced analytics database option, this tool allows users to discover insights, make predictions, and leverage useful data. With the help of such big data analytics tools, you can understand customer behavior, target best customers, and develop consumer profiles.
The Oracle Data Miner allows data analysts and data scientists to utilize the data inside the database using an elegant drag-and-drop platform.
Price: Oracle charges $460 for advanced analytics and the minimum order is 25 licenses.
Big Data Analytics Tools for Data Analysis
Though data mining is mostly about sifting through endless data sets to draw inferences, data analysis is all about breaking the data into smaller sets and assessing their impact. The concept of data analysis revolves around the idea that “analytics is all about asking the right questions and finding appropriate answers in the form of data.”
Big Data Analytics Tools: Qubole
Qubole scales, simplifies, and optimizes big data analytics workload against the data stored on Google or Azure clouds. Such big data analytics tools take the hassle out of setting up an effective infrastructure. Once the IT policies have been implemented, multiple data analysts can collaboratively “click to query” with the help data processing engines like HIVE and Presto.
Such big data analytics tools are examples of the enterprise-level solution and its flexibility sets it apart from most other analytical platforms.
Prices: Qubole offers a free trial that you can sign up for here.
Big Data Analytics Tools for Data Visualization
The primary role of data visualization tools is to make your data come to life. Generally, most data scientists/data analysts find it extremely challenging to convey insights from the data gathered to the rest of the stakeholders. For most of your bosses, MySQL databases and spreadsheets aren’t going to be enough. This is where data visualization gains acceptance among corporates as it helps visualize the data in an informative and easy-to-digest format. The best part about such big data analytics tools is the fact that these tools absolutely require no knowledge of coding.
Big Data Analytics Tools: Tableau
Tableau is one of the most famous data visualization tools that primarily focuses on business intelligence. With the help of such big data analytics tools, you can create bar charts, scatter plots, and many more without the need for programming. Tableau recently released new feature, which is called a web connector. This feature allows users to connect to a database or API; thus, giving you the option to visualize live data.
Price: It starts at $35/month.
Getting started: Big data analytics tools such as this have a lot of functionality, so check out their tutorials before getting started.
Big Data Analytics Tools for Data Integration
In the commercial space, data integration platforms allow enterprises to manage and simplify the process of sharing the large data sets that are generated. Data integration platforms are essentially the glue that hold together various programs.
In today’s connected world, every user wants to share the data and insights that they have uncovered using various tools. Therefore, if you face the need to connect the data you’ve extracted with Twitter or you want to share data on Facebook, then the data integration services given below are the right tools for you.
Big Data Analytics Tools: Blockspring
Blockspring is a unique program which harnesses the power of services like IFTTT and Zapier on common platforms such as Google Sheets and Excel. By writing a simple formula on Excel, you can connect to a host of 3rd party programs. This data integration platform also allows users to post tweets from a spreadsheet, check followers, and connect to platforms like Import.io and Tableau.
Price: Blockspring is free to use but has an organization package which allows you to create and share private functions and add custom tags for easy search.
Big Data Analytics Tools: Pentaho
Pentaho is one of the best big data analytics tools where zero knowledge of coding is required. It is a business intelligence software company that offers open source products, including data integration, OLAP services, reporting, information dashboards, data mining and extract, transform, load capabilities.
With the help of a simple drag and drop UI, you can integrate data analytics tools without coding. Pentaho also offers embedded analytics and business analytics services.
Price: You can request a free trial of the data integration product; following which, a payment will be required.
Big Data Analytics Tools: Stitch
Stitch is one of the most useful big data analytics tools that allows users to integrate, replicate, and consolidate data from across cloud services like AdRoll, Salesforce, and Drip to help you analyze everything with the tool of your choice.
Initially, the company specialized in analytics – due to which the tool is compatible with services like Tableau. It also helps in identifying the right data warehouse for your business.
Price: The pricing structure varies, but usually costs around $1,000/month.
Big Data Analytics Tools for Data Languages
Many a times in your data career, there will come a time when a simple big data analytics tools won’t just cut it. While most of the big data analytics tools available today are more powerful and easy to use, sometimes it is just better to code it yourself. Even if you are not a programmer, understanding the basics of how these languages work will give you a comprehensive understanding of how many of these big data analytics tools function and how best to use them.
Big Data Analytics Tools: R
R is a language for graphics and statistical computing. If the data mining and statistical software mentioned above do not do what you want them to execute, then learning R is the best way forward. In fact, a point to note here is that if you’re planning to be a data scientist, then knowing R is a must for all your future endeavors.
Compatibility: This program can run on Linux, Windows, and MacOS and is available for download here. Today, there is a huge community of statisticians who are using R due to its efficacy and its popularity is rapidly growing with the passing years.
How to use: Once you have downloaded the program, you can check out the documentation.
Big Data Analytics Tools: Python
Python is another language that is rapidly gaining popularity in the data community. It was created in the 1980s and is among the top ten programming languages in the world. Due to its ease of use, many journalists use Python to write custom scrapers if data collection tools fail to give them the data that they require.
Much of its popularity can be attributed to its similarities with the English language. The tool also utilizes words like “if” and “in”, which means that the script can be deciphered very easily. It also offers numerous libraries for different types of tasks.
How to use the tool: Tutorial
Big Data Analytics Tools: RegEx
RegEx, also known as Regular Expressions, is a set of characters that can manipulate and modify data. It is primarily used for pattern matching with strings, or string matching. In Import.io, you can use RegEx while extracting data to delete parts of a string or keep particular sections of a string. It is incredibly useful in data extraction since data analysts can use this tool to extract all the relevant data – which means that data analysts do not have to depend on the data manipulation companies mentioned above.
How to use: Tutorial
Big Data Analytics Tools for Data Collection
While analyzing big data, this step is one of the most crucial for inferring relevant insights. Before you can store, analyze or visualize your data, you need to get your hands on raw, unadulterated data. Data collection or extraction is all about taking data that is unstructured, such as a webpage, and transforming it into a structured table. Once the structuring has been completed, you can manipulate it in any way, using the tools that we’ve covered, that you deem fit to uncover relevant insights.
Big Data Analytics Tools: Import.io
Import.io is the world’s foremost tool for data collection or data extraction. This big data analytics platform allows users to convert websites into structured, machine readable data without the aid of coding. By using simple click and point UI, you can transform a webpage into an easy to use spreadsheet that’ll help you analyze, visualize, and use to make data-driven decisions.
Some of the best features include authenticated extractions behind a login, flexible scheduling, and fully documented public APIs. The data generated with the help of this bog data analytics platform can be utilized for machine learning, market and academic research, lead generation, app development, and price monitoring activities.
Price: Such big data analytics tools offer a free trial of the full product and a free version as well.
Getting started: Check out their knowledge base to learn more about how to use the tool.
Hope you enjoyed this round-up of big data analytics tools!