13 Top Picks
39 Hours of Research
34 Options Considered
Companies use the best big data analytics tools to predict and determine behavior on a large scale to help you make decisions and see how they will affect this grouping. These can help companies reduce operating costs, offer better products and services, and see how their consumers are spending that result in a more profits and growth. So whether you have your own data pipeline and warehouse or you are using big data analytics vendors you can rest assured our recommended software is robust and powerful enough to help you extract value from your big data.
3 Top Big Data Analytics Software
Tableau is extremely powerful. The fact that it is one of the most mature and powerful options available shows as soon as you see the available features. It’s a bit steeper to learn this platform, but once you do it is well worth it.
2. Zoho Analytics
Zoho Analytics is a really nice system. They’ve been around for a long time when it comes to CRM and predictive data. They can handle everything from analytics to sales.
Splunk is a great option for a lot of different people. It can handle small, midsized, and large business enterprise data as well as public administrations and nonprofits.
Best Big Data Analytics Tools Reviews
In this article, you will learn more about big data software services and the best products. We will cover all of their strengths and weaknesses so that you know exactly which one will work best for your needs. By the end of this article, you will know which of the top 13 big data software options is right for you, or whatever big data analytics consultancy you are partnering with.
Tableau is hands down one of the big data systems & software available. The fact that it is one of the most mature and powerful options available shows as soon as you see the available features. It’s a bit steeper to learn this platform, but once you do it is well worth it. If you want the absolute best go with Tableau. No need to waste thousands of dollars and expensive engineering & data analyst hours on trying sub-par programs.
Tableau has been around since the early days of data analytics tools, and it continues to mature and grow with the industry. It is extremely intuitive and offers comprehensive features. Tableau can handle any amount of big data, no matter the size. It can also handle both structured and unstructured data, unlike older, traditional big data management tools
It also offers customizable dashboards and real time data in visual methods for exploration and analysis.
This big data analytics tool can blend data in powerful ways because of how flexible the settings are. It has tons of smart features and works at lightning speed. Best of all, it is interactive and can even work on mobile devices and share data through shared dashboards.
2. Zoho Analytics
Zoho Analytics has been around for a long time in the CRM and predictive data space and is one of the most popular business analytics softwares. They can handle everything from analytics to sales. This platform offers a great mix of all its features at a very affordable price.
Zoho Analytics doesn’t just stop at big data analysis. Their product is very affordable and offers a ton of great features.
Comparing Zoho Analytics vs SAS Visual Analytics - Zoho is more robust and powerful, with advanced analytics. The amount of features can be overwhelming compared to SAS Visual Analytics, which has a simpler interface but isn't as powerful. If you have an in-house data team and want to grow it then Zoho is ideal while SAS Visual Analytics might be better for a smaller team.
They offer big data integration with Google, extremely valuable big data analysis reports, and tons of great marketing campaign tools.
Their user interface is pretty big, but that’s because there are a lot of great features. Their advanced analytics can run your data through sales funnels and other reports. You can manipulate your data and log it over long periods of time to track.
Zoho Analytics Pros
Zoho Analytics Cons
Splunk is a great option for a lot of different people. It can handle small, midsized, and large business enterprise data as well as public administrations and nonprofits. A single user license works for 1,000 to 4,999 and it can be deployed on premises, through mobile devices, or even through the cloud!
This technology is able to collect data then index it, which will allow you to search and sort it. It can also analyze and report the data and uses some very beautiful visual reporting features.
It will automatically notice if any of the data patterns are abnormal and let you know thanks to the real time monitoring feature.
It also allows you to customize your interface, which makes it a breeze to use. All of your most common tools can be shown right upfront at any time you need.
It also offers operational visibility, no matter what environment you’re using for access. It can be scaled and adapted to fit the needs of any organization.
4. SAS Visual Analytics
SAS Visual Analytics makes it easy to analyze and share the type of powerful insights companies need into their data. This is one of the better options for anyone who needs an easy user interface and doesn’t mind paying for the convenience.
This software is great when it comes to creating visual displays and representations of your data. Businesses can use them to show their analyses in different meetings and help different departments understand how it all ties together.
This program is also capable of data mining and statistical modeling. This makes data analysis even easier. Predictive models can help with forecasting and estimation of risks and rewards for various marketing campaigns.
Their models all take their basis in statistical algorithms. They choose the relevant algorithm to the predictive model and run the test to see how the data performs. It’s a beautiful layout because you can develop different dashboards for different projects allowing management to see everything separately and make their decisions.
SAS Visual Analytics Pros
SAS Visual Analytics Cons
Talend is a great option, too. It’s an open studio that can handle tons and tons of data using a free license. It uses NoSQL and Hadoop Distributed File System to connect everything together.
Talend is a massive data platform. You can use a subscription license to handle the components and connections. It works with Spark and MapReduce. This platform can offer email, phone, and web support to licensed users, so you can always call with any questions, which makes up for the iffy user interface.
This also offers real time data. It can connect with Spark streaming, IoT, and machine learning. The open studio is completely free. The rest of the products within the suite offer subscription based costs that are flexible based on your specific needs.
Useful features include the real time moves and the rapid speed of results it can attain. Whether you need multiple data sources or multiple connectors, Talend can get the job done with ease.
Apache's Cassandra is another free tool to use. The open sourced software can manage massive volumes of data thanks to the NoSQL DBMS that was constructed. It can even handle data that has been distributed across a variety of different commodity servers. It has its own CQL (Cassandra Structure Language) to interact with its database.
Cassandra has been picked up by some very high profile companies because of how well it works. Facebook, American Express, Accenture, Yahoo, General Electric, and Honeywell all use this big data analytics tool. It doesn’t have any single point of failure anywhere in the awesome set of features, which include simple ring architecture, automated replication, and easy log structured storage.
Although troubleshooting and maintenance can take a little more effort than other big data analytics tools, the free price makes it worth it. Mix that with the fact that it has such a rapid response time, and doesn’t take too much system resources, and you’ve got a winner!
SiSense is a great option that is embraced by a lot of very seasoned business intelligence (BI) tool users because it has so many comprehensive features. It’s a great option for just about all of your needs.
SiSense is built with a couple of different parts. It has a web interface that is incredibly intuitive and also uses ElastiCube, which is their proprietary database for analyzing data. You have to download ElastiCube and run it on a local computer, but it’s pretty easy to use.
SiSense understands that a big platform has a big learning curve, too, so it took some steps to make it easier. Before you run your data, you can take the ElastiCube tutorial. This will let you mess around with the data and test a ton of different features.
Although a lot of platforms offer tons of different visualizations, which tend to become selling points, SiSense foregoes the vast number of design options in favor of deeper insight. Their widgets will offer visualizations that are interactive and let you drill to anywhere you need to inspect different areas with a simple mouse click.
Apache Spark is a great open source option for people who need data analytics tools on a budget. This data analytics engine's speed and scalability have made it very popular among data scientists.
One of the great things about Spark is how compatible it is with almost everything. It can also be used for a variety of different things, like cleansing and transforming data, building models for evaluation and scoring, and determining data science pipelines for production.
The lazy execution is really nice. This feature allows you to set a series of complex transformations and have them represent as a single object.
This allows you to inspect its structure and end result without executing individual steps along the way. Spark even checks for errors in the execution plan before submitting it, which prevents bad code from taking over the process.
Pivoting data is one of the biggest challenges of many major data frameworks. It usually requires several different case statements to execute in SQL. Spark makes it very easy to pivot your data frames using a “groupby” on your target index column.
Plotly is one of the most visually appealing big data analytic tools available. It is a cloud based solution for data science and interpretation that allows you to modify, synthesize, and distribute your information graphically in a way that can be interacted with anywhere on the web.
Plotly uses Python framework to run, so it can handle analytics, visualization, and more with ease. It has tons of equipment to help you plot statistical data for easy analysis. It can also handle scientific graphing libraries. Arduino, Python, REST, Perl, R, MATLAB, and Julia are all compatible with Plotly.
The system has tons of benefits, too. Plotly allows businesses to create their own data visualization platforms. This allows them to integrate these beautiful graphs into their reports, programs, products, and analyses. Charts are easy to create without needing to know any code!
Open source is willing all the awards in the best-of category for data analysis, and Cloudera is no exception to the rule! Cloudera is focused on enterprise level deployments of technology. It is open sourced and the platform distribution is completely free.
Cloudera has a full platform that the open source software can encompass. The suite includes Apache Hadoop, Spark, Impala, and other great products and tools. With this option, you can process, collect, manage, administer, discover, distribute, and model an unlimited amount of data!
If you’re interested in the Hadoop cluster, just know that their licensing price is on a per-node basis, so it can get very expensive.
Still, the system allows you to use tons of great things. This is entirely scalable storage with real time access to read and write. Hive is a familiar framework of SQL that offers metadata and batch processing. Accumulo will store and serve performance intensive applications in a very secure location. Flume can collect and aggregate event data and log it in real time.
Skytree is a great big data analytics tool. It allows users and data scientists to create extremely accurate models very quickly. Their predictive machine learning models are extremely intuitive and make experimentation and data manipulation very easy.
Skytree offers tons of great features. Their algorithms are very scalable. This means that whether you’re a small business or entrepreneur, or a giant enterprise, you can use their models and know that they will scale to fit the size of the data that you input.
They employ a machine learning technique that allows artificial intelligence to learn and communicate with data scientists.
The platform was specifically designed to handle very complicated problems.
It was given the ability to handle predictive behavior problems and create accurate models so that data scientists can manipulate the models and determine how different points will affect the aggregate data.
Elasticsearch is another great open source option. It works across multiple platforms and can distribute data easily. It even has a Lucerne based search engine. This is one of the most popular enterprise search engines on the market today.
Elasticsearch is included as an integrated solution with Logstash and Kibana. Logstash can collect data and parse logs and Kibana is a great platform for the visualization of data and analysis. The three products work together in what is known as Elastic Stack.
A lot of people don’t like open source software because it can be difficult to follow when there’s no one to call for tech support. Happily, Elastic has a very active community and their documentation is incredibly easy to understand. It makes it super easy to use the NO-SQL search engine and storage. Elastic also has APIs for just about anything you will ever need.
Lumify is a nice program to use. Although it isn't the best, it's a decent option for big data fusion and integration if you're on a budget and don't have money for subscription licenses under open source platforms.
Lumify has some great features, even for being an underdog in the list, which is why it beat out a few other options to make it on our list. It offers graphing in both 2D and 3D visual models and even includes several automated layouts.
It also comes with a lot of different options that can analyze the statistically significant links between the data points on the graph.
Lumify can also offer ingest processing and interface elements between the different options, specific to images, videos, and text based content. It has a feature called Spaces that lets you organize all of your data into different projects and workspaces to keep track of them. The foundation of Lumify is a big data technology that has proven scalability.
Buying Guide to Best Big Data Analytics Tools
Let’s take a look at some of the big data and analytics requirements. These are some of the features that the data analytics tools software will need to highlight
Professional Statistical Analysis
Whether you are analyzing network packets or looking at large volume customer data sets, big data solutions need to include professional statistical analysis. This includes the normative distributions. These big data analytics tools can use specific algorithms to classify clusters. They perform regressive analyses based on this data. Basically, they take data sets and use them to simulate experiments.
These big data analytics programs allow you to answer questions without having to conduct physical studies. For example, if there is a normal distribution of data, then can you find triangular patterns in your distribution? These tools can help you figure out the discrete situations and whether these situations fall within a desirable statistical controllable range. They will inform you of any magnifying affect your parameters may contain and allow you to run hypothetical simulations and analyze results. They can also help you determine the impact any single parameter has on data results.
Individual Predictive Analysis
Good data analysis software will be able to predict and analyze the behavior of individual consumers. These tools would be able to tell you the length of time they spend somewhere, their spending habits, whether or not their credit is good and whether to loan to them, their online consumption record, and more.
The tools could also be used to push different items to the customer based on their browsing history. This allows you to increase the effectiveness of ad campaigns and feel like you’re marketing directly to the consumer. This uses concepts from artificial intelligence and machine learning to predict individual behavior.
What Are the Types of Analytics on Big Data?
There are four main types of big data analytics: diagnostic, descriptive, prescriptive, and predictive analytics.
When would you use big data tools?
In traditional databases, processing a vast volume of data can be quite challenging. As a result, you can easily use Big Data Tools to manage your data. Big Data Analytics can help you make better and quicker decisions, model and forecast future events, and improve your Business Intelligence.
1. What are the best big data analytics tools and software?
These are the top 3 Big Data Analytics Software that we can recommend.
Tableau - Tableau is extremely powerful. The fact that it is one of the most mature and powerful options available shows as soon as you see the available features. It’s a bit steeper to learn this platform, but once you do it is well worth it.
Zoho Analytics - Zoho Analytics is a really nice system. They’ve been around for a long time when it comes to CRM and predictive data. They can handle everything from analytics to sales.
Splunk - Splunk is a great option for a lot of different people. It can handle small, midsized, and large business enterprise data as well as public administrations and nonprofits.
2. What are examples of big data?
Examples of Big Data in the Real World
1. Identifying consumer purchasing habits.
2. Marketing that is tailored to the individual.
3. Tools for the transportation industry's fuel optimization.
4. Monitoring health status using data from wearable devices.
5. Road mapping in real time for autonomous vehicles.
6. Streaming media in a more streamlined manner.
7. Inventory ordering using predictive analytics
3. What tools are required for big data analysis?
In order to analyze big data, one requires various tools. These tools vary in purpose, but many are necessary for data cleaning, exploration, modeling, and deployment. Some popular big data tools include Splunk, Zoho Analytics, Hive, Tableau, and Spark.
4. What qualifies as big data?
The term Big Data refers to a dataset that is either too large or too complex to be processed by conventional computing devices. As such, it is relative to the market's available computing power. If you look at recent big data history, we had 1.5 exabytes of data in 1999 and 1 gigabyte was considered big data.
5. What are big data tools?
Big data tools refers to the various software and programming languages that are used to manage and analyze big data sets. These tools can help organizations make better decisions by allowing them to more easily identify patterns and trends in their big data. Some of the most popular big data tools include Hadoop, Spark, and SQL, but there is a lot, which is why you should do a careful big data tools comparison to see which one fits your company's needs.