Big Data Analytics Tools – As we know, in today’s developing technology, data is very important. Moreover, the data we generate when we are active online continues to double every day. To process large amounts of data ( big data ), a big data specialist needs to use big data analytics tools.
Big data specialist is a term that covers various professions related to data, such as data engineers, data scientists, data analysts, data architects, and database administrators. In this article, we will discuss 10 tools that are used for big data purposes , watch until they run out!
What is Big Data Analytics?
Big Data analytics is a process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Big Data analytics provides various advantages—it can be used for better decision making, preventing fraudulent activities, among other things.
Data is meaningless until it turns into useful information and knowledge which can aid the management in decision making. For this purpose, we have several top big data software available in the market. This software help in storing, analyzing, reporting and doing a lot more with data.
Big Data has become an integral part of businesses today and companies are increasingly looking for people who are familiar with Big Data analytics tools. Employees are expected to be more competent in their skill sets and showcase talent and thought processes that would complement the organizations’ niche responsibilities. The so-called in-demand skills that were popular so far have been done away with and if there’s something hot today, it’s Big Data analytics.
Best Big Data Analytics Tools
Big data analytics tools are solutions that pull data from multiple sources and prepare it for visualization and analysis to discover deeper business insights into trends, patterns and associations within data. Big Data Analytics is a process that enables data scientists to make something out of the stack of big data generated. This analysis of big data is done using some tools that we reckon as big data analytics tools.
In this blog, we will be discussing the top 50 big data analytics tools (in no particular order) that are being leveraged by data scientists.
1. Hadoop
Hadoop helps in storing and analyzing data and is considered to be one of the best tools to handle huge data. It is written in Java and is an open-source framework. Right from plain text, images to videos, Hadoop stands the potential to hold it all. It is highly scalable and finds immense application in the field of R&D. MongoDB – used on datasets that change frequently
2. Talend
Talend is used for data integration and management. Talend is the leading open source integration software provider to data-driven enterprises. Our customers connect anywhere, at any speed. From ground to cloud and batch to streaming, data or application integration, Talend connects at big data scale, 5x faster and at 1/5th the cost. Cassandra – a distributed database used to handle chunks of data
3. Apache Spark
Apache Spark is one of the most powerful open source big data analytics tools. It is a data processing framework that can quickly possess very large data sets.
It can also distribute data processing tasks across multiple computers, either on its own or in conjunction with other distributed computing tools. Apache Spark features in-built for streaming, SQL, machine learning, and graph processing support and earns the site as the speediest and common generator for big data transformation.
4. MongoDB
MongoDB is a free and open-source data analytics tool that is known to provide support for multiple technologies and platforms. It also supports multiple operating systems including Windows Vista and Linux. Also, MongoDB is easy to learn, reliable and economical – all at the same time.
5. Pentaho
Pentaho addresses the barriers that block your organization’s ability to get value from all your data. The platform simplifies preparing and blending any data and includes a spectrum of tools to easily analyze, visualize, explore, report and predict. Open, embeddable and extensible, Pentaho is architected to ensure that each member of your team — from developers to business users — can easily translate data into value
6. STORM
Apache Storm is a cross-platform, distributed stream processing, and fault-tolerant real-time computational framework. It is free and open-source. The developers of the storm include Backtype and Twitter. It is written in Clojure and Java.
Its architecture is based on customized spouts and bolts to describe sources of information and manipulations in order to permit batch, distributed processing of unbounded streams of data.
7. Xplenty
Xplenty is known for integrating and processing data for analytics on the cloud. It boasts of an intuitive graphic interface and a cloud platform that is highly scalable and elastic. This data analytics tool doesn’t invest in hardware, software, or related personnel to transform raw data. Xplenty is extensively used in the field of marketing, sales, support, and developers.
8. Apache Cassandra
Big tech giants like Facebook, Accenture, Yahoo, etc. rely on Cassandra. This is an open-source framework that is known for managing huge data volume in the least possible time. Two features that make Cassandra stand apart from the rest are linear scalability and the fact that this data analytic tool is free.
9. CDH (Cloudera Distribution for Hadoop)
Cloudera aims at enterprise-class deployments of that technology. It is totally open source and has a free platform distribution that encompasses Apache Hadoop, Apache Spark, Apache Impala, and many more.
It allows you to collect, process, administer, manage, discover, model, and distribute unlimited data.
10. Microsoft Azure
Microsoft Azure, formerly known as Windows Azure, is a public cloud computing platform handled by Microsoft. It provides a range of services that include computing, analytics, storage, and networking.
Windows Azure provides big data cloud offerings in two categories, Standard and Premium. It provides an enterprise-scale cluster for the organization so that they can run their big data workloads.
11. Zoho Analytics
Zoho Analytics is a BI and Data analytics software platform that helps its users to visually analyze data, create visualizations, and get a better and in-depth understanding of raw data.
It allows its users to integrate multiple data sources that may include business applications, databases, cloud drives, and more. It helps users generate dynamic, highly customizable, and actionable reports.
12. Splice Machine
Splice Machine is a scale-out SQL Rotational Database Management System (RDBMS). It has ACID transactions, in-memory analytics, and in-database machine learning, combined.
The big data analytics tools can scale from a few to thousands of nodes enabling applications at every scale.
13. Python
Right from data cleaning, data modelling, data reporting to building analysis algorithms, Python has got you covered. Python is a relatively easy tool to work on. I addition to being user-friendly, Python is known for its portability. There are numerous operating systems that Python supports and one can work on them without making any changes to the system.
FAQ
Q: What separates big data analytics from regular data analytics?
A: The defining characteristic of big data is that the entire dataset is too big to fit into your analytics software (or your computer) all at once. Depending on who you ask, “too big” might range from only a million rows of data to 20 million or so.
If you routinely find yourself staring at data that’s got millions of observations, though, you are definitely crossing into the realm of big data analytics. Big data offers the opportunity for more sophisticated analysis techniques, but also poses challenges for straightforward analysis. Big data analytics software helps add a level of abstraction, so you don’t have to load and operate on the entire dataset all at once.
Q: How do you use big data for marketing?
A: For marketing, the most helpful insights you can get from big data are predictions on customer preferences, and predictions on customer purchasing tendencies.
Big data gives you a huge leg up compared to generic marketing email blasts: if you can identify the users who are most likely to make a purchase, and set them up with a custom marketing campaign that recommends the products they are most likely to buy, you can see huge gains in your click-through rates and your conversion rates.
To use big data in this way, make sure you have both “input” and “output” data on your customers: who are they, what do they like, and what do they ultimately buy? If you have this data in-hand, any competent data scientist should be able to build a predictive model using your big data tools to improve your marketing campaigns.
Q: Is big data the same thing as data science?
A: Big data often goes hand in hand with data science, but they aren’t synonymous—data scientists often work with big data, but they might find themselves working with small datasets too. Likewise, if you have a huge dataset that is not particularly complex, it may not be that interesting to a data scientist; you might pass it off to a business analyst instead.
At most large companies, though, data scientists spend most of their time working with big data. That’s because big datasets enable you to use complex machine learning and artificial intelligence algorithms that can improve the accuracy of your predictions, but that require huge amounts of data to properly develop.
Q: Do you have to use machine learning to analyze big data?
A: Machine learning is very popular for analyzing big data, because large datasets can unlock the full potential of complex, sophisticated machine learning algorithms.
However, it’s not strictly necessary: sometimes all you need to do to analyze big data is a simple statistical model, or even a plot that summarizes the most important trends in your data.
Occasionally, these simple tools can be more useful than a fancy machine learning model, especially when the fundamental business questions you are asking are straightforward.
Q: Can big data analytics help with fraud detection?
A: Fraud detection was one of the first applications of big data analytics tools, particularly a type of analysis called anomaly detection. Banks, for example, have huge databases of financial transactions, but only a tiny fraction of them are known to be fraudulent.
By using big data tools, banks can build models that detect deviations from the typical spending patterns of a particular customer, flagging unusual purchases for review by the fraud team. You can use a similar strategy for your business by building a large dataset of genuine transactions, and at least a small number of fraudulent transactions.
Even with only a few cases of fraud, it’s possible to build algorithms that can flag them, assuming you have enough data to model the typical patterns seen in genuine transactions.
Q: Can you use big data analytics tools with protected health information?
A: Yes, big data analytics is very popular with health insurance companies, hospitals, and doctor’s offices, but you need to take some extra steps to make sure your system is compliant with regulations that govern protected health information, or PHI.
The key phrase to look for is “HIPAA Compliant,” which refers to a US federal law that governs protected health information. Making sure you set up a big data system that is HIPAA compliant is important, because your company could be held legally liable if you don’t appropriately follow the regulations.
Q: Do you need to be able to program to analyze big data?
A: A lot of big data analysis is built around programming, whether that is SQL queries, scripts for R and Python, or setting up APIs to transfer data.
However, if you’ve already got a team that manages your data and stores it in a data warehouse, there are several user-friendly tools that make it possible to analyze big data without programming abilities.
Salesforce Einstein and Tableau are just two examples of tools you can use to analyze big data without any real programming.
Q: Is data mining the same thing as data analysis?
A: Data mining is a particular type of data analysis that seeks to uncover new or interesting patterns in large amounts of data. Usually, data mining isn’t done with a specific question in mind, which puts it in contrast with traditional data analysis.
If you want to know which of your factories are producing the most products every day, that is a traditional data analysis question. If, on the other hand, you want to uncover patterns in your supply chain that affect factory output, that might fall more into the realm of data mining—you don’t have a specified, a priori question, and you may or may not find something new and useful.
Data mining tends to require bigger databases and more creative and sophisticated analysis, which is why it tends to be associated with big data.
Q: What is Kylo?
A: Organizations have tried to build complex, custom-engineered and Hadoop-enabled solutions in-house that often lack governance, security and quality control. These complex projects have become too costly and time consuming, resulting in business users losing interest and significant loss of the investment.
With these challenges in mind, Think Big has built Kylo™ on eight years of global expertise involving 200+ data lake projects in global banking, telecoms, retail and more. Kylo™ is a solutions platform for delivering data lakes on Hadoop and Spark. The benefits of Kylo™ include:
- Scale for the future: makes it easy to scale data lakes to large numbers of data feeds with a template approach and a visual interface to simplify creating and modifying them with security built-in
- Easy to use: includes an intuitive user interface for self-service data ingest and wrangling (no coding required!), allowing more IT professionals to access the data lake
- Metadata management: provides metadata tracking, allowing data stewards and data scientists to quickly catalog, discover and qualify data and understand the accuracy of data
- Operational monitoring: offers an operations dashboard for SLA tracking and feed monitoring
- Best-of-breed technology: built on modern open source frameworks such as Apache Spark and Apache NiFi
The Kylo™ journey is a three step process that addresses the key stages of data lake ingestion, transformation and discovery:
- Ingest: there are many tools that ingest batch data, but few that will work to ingest streaming or real-time data. Kylo™ supports a mixture of both.
- Prepare: using Kylo™, companies are able to pull apart and understand their data better. Kylo™ helps to cleanse data in order to improve its quality and to accredit data governance.
- Discover: once your data has been ingested, cleansed and installed in the data lake, your analysts and data scientists can begin to search and find what data is available to them. Kylo™ makes this data discovery simple, allowing users to build queries to access the data in order to build data products that support analysis.
