Top 10 Tools to Analyze Big Data That Will Help You Make Sense of Your Data

Top 10 Tools to Analyze Big Data That Will Help You Make Sense of Your Data

Dorota Owczarek - December 21, 2022

Big Data has taken the world by storm. Thanks to this technology, businesses of all industries and sizes can now collect far more data than ever before and then use it to tailor their products, strategies, and customer support.

Extracting useful information from vast amounts of data isn’t an easy task, though. That’s where Big Data tools come in handy for analyzing even zettabytes of data in the blink of an eye and turning them into useful information about your business and customers.

What even is Big Data, and why do you need specialized tools to manage it? This article will cover everything you need to know, including our top ten incredibly useful tools to analyze Big Data that you might want to try out.

The Necessity of Big Data Analytics

Big Data is a term describing massive datasets of structured, unstructured, and semi-structured information collected from various sources and then turned into useful, actionable insights through data integration and processing. Massive, as in petabytes (1,024 terabytes), exabytes (1,024 petabytes), or even zettabytes (1,024 exabytes) of stored data.

Why is it that we have so much data to manage nowadays? Look at a few statistics:

Basically, anything we do online nowadays produces data – to be more precise, 2.5 quintillion bytes of data every day on average (and that was back in 2018). For businesses, this data can be immensely valuable, as it can tell them far more about their business performance and customers’ needs than ever before.

It can even let them carry out predictive analytics on what their customers might want in the future. That’s why, according to a Findstack survey, 97.2% of organizations are now investing in Big Data – to help them become more efficient and understand their audiences better.

The main problem when it comes to using Big Data to its full potential is that it is quite different from “traditional” data. Those differences can be best explained through the 5 Vs, or the five inmate traits of Big Data.

Volume

The size of the collected data sets that need to be analyzed and processed. Big Data is, well, big – volumes of data are now typically measured in terabytes, petabytes, or exabytes and can include billions or even trillions of records. The sheer volume of data makes it nearly impossible to manage and analyze the data manually or through regular data tools.

Value

The insights gained from Big Data sets can be immensely valuable for businesses. After organizing and scanning the data for patterns, companies can learn far more about their performance or customers’ needs and issues, then use that data to improve business strategies or products.

Variety

Big Data comes from many different sources, such as business processes, audio and video files, search engines, social media platforms, smart devices and machines, integrations with external tools and SaaS platforms, and many more. That lets companies build a complete profile of how their business works and who their customers are.

Velocity

The speed at which the data is created, updated, shared, and processed is another trait of Big Data. Velocity is also used to describe how fast the sets are growing in volume.

Speed also has one additional metric - latency. Data latency is the time it takes for data to be transferred from its source to its destination. It can be thought of as the delay between when an action is taken and when the resulting data becomes available. Data latency can be affected by a variety of factors, including the distance between the source and destination, the speed of the connection between the two, and the amount of traffic on the network. In some cases of big data processing and analytics, data latency can be critical, especially in applications where real-time data is required, such as financial trading or fraud detection. In these cases, even a small delay can have significant consequences. In other cases, data latency may not be as important, and a longer delay may be acceptable.

Veracity

Veracity refers to the trustworthiness and quality of the data. Since it is collected from multiple data sources, it needs to be checked for reliability and accuracy first and then cleaned of errors. Using outdated, inaccurate, or meaningless data could lead business owners to make bad decisions that then impact their business growth, revenue, and reputation.

The characteristics of Big Data make it quite tricky for regular data tools to process and extract useful information – there’s simply too much data for traditional tools to handle. Moreover, a vast proportion of Big Data (usually from 80% to 90%, but numbers differ from business to business) is unstructured, meaning data in various formats and types.

Traditional data tools work best when they have the data in the same format and type, with other types that do not fit the structure being omitted. However, it’s impossible to fit all of that unstructured data into the requirements, rendering standard data tools barely usable now.

Features of Big Data Tools

Big Data analytics is not a single process. Instead, it is a combination of several processes and pipelines designed to turn raw data into actionable, valuable information for businesses.

Big Data analytics pipeline

Big Data analytics pipeline

Because of this, Big Data platforms usually include multiple tools and features that enable companies to take full advantage of all the available information without having to process big data manually. Below are some especially handy features.

Batch Processing

Batch processing is a very efficient method of processing large amounts of data, especially when businesses don’t need the analyzed data immediately. Basically, the Big Data platform collects a given type of data for a set time and then automatically processes everything at once, often when the system is idle.

A great example of using batch processing is handling payrolls or billing activities, which typically only need to be processed once a month.

Real-Time Stream Processing

Real-time data processing is pretty much what it says on the tin – gathering, processing, and updating data right after it is received by the platform. The system doesn’t wait until it has enough data or there are no other tasks to process but instead analyzes each piece of information as soon as it appears inside the system.

That way, the information coming from the raw data is available almost immediately. There are numerous applications where real-time processing is vital – streaming data, radar systems, and customer service systems, just to name a few.

Graph Processing Features

Analyzing the relationship between various data points was a pretty complicated task, especially when the data sets were large. Graph processing is a type of data analysis that focuses on the relationships between data elements rather than just the individual data elements themselves. In the context of big data analytics, graph processing can be used to identify patterns and trends in complex datasets and to make predictions about future events or outcomes.

Tools with graph processing features don’t have the slightest problem running this type of computation – they can quickly analyze the connection between different data sets, spot patterns, and then highlight all of them.

Machine Learning Features

Machine learning is now one of the most valuable features of Big Data tools since this technology – a branch of data science – can be of great assistance when it comes to processing and then making sense of the stored data.

In fact, the large volumes of Big Data available are incredibly useful for ML – the more data the system has to train on, the better it can understand patterns and make predictions, classifications, recognition, or generation based upon them. Plus, tasks like building analytics models or generating insights from historical data can now be fully automated, saving companies plenty of time.

Data Visualization

Which would be easier for you to understand, a spreadsheet containing multiple metrics or a graph with the same information? Most likely, you picked the latter. It can be pretty time-consuming to create detailed graphs, charts, or maps by hand though.

On the other hand, big data analytics tools can turn the data they have inside their system into charts or graphs in a matter of seconds, no matter how large the data sets are. What’s more, these solutions usually come with dozens of visualization design tools that allow you to adjust how the charts or graphs look.

Data Storage and Management

Storing and managing enormous amounts of data is another common problem, especially since the volumes only keep growing in size. Thanks to Big Data services running as cloud services, though, companies can easily store the data without needing to invest in additional equipment.

Besides dedicated storage services for businesses that can be extended to virtually unlimited capacity, big data frameworks are usually horizontally scaled, meaning that additional processing power can be easily added by adding more machines to the cluster. This allows them to handle large volumes of data and to scale up as needed to meet the demands of the workload. In addition, many big data frameworks are designed to be distributed and parallel, meaning that they can process data across multiple machines in parallel, which can greatly improve the speed and efficiency of data processing.

Support For Various Data Formats

There are several storage and compression formats available for big data, with each of them being most suitable for different use cases. For example, you might want to store raw data in one format but, after processing, use it as a different type.

That’s why it’s essential that the Big Data tool you pick will be able to read and analyze data in various formats, such as CSV, JSON, AVRO, ORC, or Parquet. Otherwise, you might need to spend time converting the files into the required format first, which would be both time-consuming and pretty risky when it comes to data integrity.

Best Big Data Tools That You Can Use Today

Now that we’ve talked a bit about what Big Data even is and what Big Data platforms do, let’s see some examples of the best tools you can use to analyze your data. We’ll start with open-source big data frameworks coming from Apache Foundation that are available for free, and then we’ll move to a few of the best-known solutions offered by cloud providers as dedicated, fully managed services for big data analytics.

Open Source Frameworks

Apache Spark

Apache Spark is a free big data framework for distributed processing, designed as an alternative to Hadoop. Using Spark, data can be stored and processed through a network of multiple nodes that can work on the data in parallel, making data processing much faster.

Apache claims that Spark runs 100 times faster than Hadoop’s MapReduce and can work through 100 terabytes of big data in a third of the time Hadoop needs to process the same volume.

Spark architecture with HDFS, YARN, and MapReduce

Spark architecture with HDFS, YARN, and MapReduce

If you’re looking for a more comprehensive article on Apache Spark, head over to our recent article on this stream processing framework - What is Apache Spark? Architecture, Use Cases, and Benefits.

Another Apache open-source big data technology, Flink, is a distributed stream processing framework that allows for the examination and processing of streams of data in real time as they flow into the system. Flink is designed to be highly efficient and able to process large volumes of data quickly, making it particularly well-suited for handling streams of data that contain millions of events happening in real time.

One of the key features of Flink is its ability to process data in real time, which means that it can analyze and work on data as it is received rather than having to wait for all of the data to be collected before beginning processing. This allows Flink to provide fast and accurate results, even when dealing with large volumes of data.

In addition to its speed, Flink is also known for its ability to scale horizontally, meaning that it can easily add more processing power as needed by adding additional machines to the cluster. This makes it well-suited for handling high volumes of data and for use cases that require the ability to quickly and easily scale up as needed.

Apache Flink architecture and key components

Apache Flink architecture and key components

If you want to learn more about Apache Flink, head over to our recent article on this stream processing framework - What is Apache Flink? Architecture, Use Cases, and Benefits.

Apache Hadoop

Apache Hadoop is currently one of the most popular and widely used Big Data frameworks for batch data processing. The app is best known for its Hadoop Distributed File System (HDFS), which allows companies to hold any type of data (video, images, JSON, XML, and plain text) inside the same file system.

Hadoop also automatically replicates the data stored on the Hadoop network cluster to ensure that the data is still available even if one of the computers in the network fails.

Big data architecture with Kafka, Spark, Hadoop, and Hive for modern applications

Big data architecture with Kafka, Spark, Hadoop, and Hive for modern applications

Apache Hive

Hive is a data warehouse tool for reading, writing, and managing data sets stored directly in Apache HDFS or other data storage systems like Apache HBase.

Developers like this app a lot because rather than spend time writing MapReduce queries in lengthy Java code, they can use Hibernate Query Language (HQL). Hive will then automatically transform them into MapReduce tasks.

Apache Storm

Apache Storm is a distributed real-time computation system for processing high-velocity data. Storm is extremely fast, being able to process over a million records per second per node. The biggest benefit of using Storm, though, is that topologies can be created in multiple different languages, meaning developers can write in whichever language they are most familiar with.

Apache Kafka

Kafka combines messaging, storage, and stream processing to store, analyze, then share historical and real-time data to different places. Furthermore, the data inside Kafka can be distributed across as many servers as needed, making it incredibly scalable.

Kafka Streams is a stream processing library that is built on top of Kafka and provides a simple and easy-to-use API for developing stream processing applications. It allows developers to build real-time, scalable, and fault-tolerant stream processing applications that can process data from Kafka in real-time.

One of the key benefits of Kafka is its ability to handle high volumes of data with low latency, making it a popular choice for use cases such as real-time analytics, event-driven architectures, and data integration. It is also highly reliable, with strong support for distributed systems and the ability to handle failures without losing data.

Kafka’s Connect interface can also be integrated with hundreds of event sources and data lakes, such as Postgres, JMS, Elasticsearch, AWS S3, and more.

Kafka architecture - Kafka message flow through components

Kafka architecture - Kafka message flow through components

Apache Cassandra

Apache Cassandra is a NoSQL database that stores data for applications requiring fast read and write performance. Unlike traditional databases that require all data added to them to be structured, NoSQL databases also accept unstructured data.

What’s more, databases can be quickly scaled up or down depending on the business’s needs just by adding or removing nodes.

Big Data Solutions from the Biggest Cloud Providers

If you are looking for a fully managed service for big data analytics, you have a range of options available from major cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). These services provide everything you need to get started with big data analytics, including dedicated infrastructure, support for a variety of data sources and storage options, and a range of tools and features for data ingestion, transformation, analysis, and visualization.

One of the key benefits of using a fully managed service is that it takes care of many of the technical details for you, allowing you to focus on your data and analytics needs. These services are designed to be highly scalable and reliable, with the ability to handle large volumes of data and support a wide range of workloads. In addition, they typically offer a range of pricing options, allowing you to choose the solution that best fits your needs and budget.

AWS – Amazon Athena

Amazon Web Services also has their own Big Data tool as a part of their web services. Athena is an interactive query service that allows users of Amazon Simple Storage Service (S3) to analyze large amounts of data directly. Athena can also process both regular, structured data and unstructured or semi-structured data.

The platform is serverless by design, meaning there’s no need to set up, manage, or update in-house infrastructure to run the platform – Athena handles all of those tasks.

Users are charged for the number of queries and amount of data scanned by the application during a month, from $5 per terabyte.

Azure – Data Explorer

Another big data analytics service that companies frequently pick is Azure’s Data Explorer. As it works as a Platform-as-a-Service (PaaS), you can embed the tool into your application and then have near real-time analytics capabilities available straight away.

For example, the Azure SQL Database team uses Data Explorer to monitor how their service works, find any performance anomalies, and then troubleshoot to prevent service issues.

Compared to Amazon’s Athena, Azure Data Explorer charges per hour of use rather than per query, with the cost depending on the chosen tier and capacity. The pay-as-you-go plan costs $0.11/core per hour. However, on an annual plan, the hourly cost reduces to $0.0935/core or $0.0771/core on a three-year plan.

Google Cloud Platform – BigQuery

With a built-in query engine capable of running SQL queries on terabytes of data in seconds or on petabytes in minutes, Google BigQuery is one of the fastest available data warehouses. It is also serverless and highly scalable.

BigQuery offers a wide variety of features inside the service as well, such as analyzing data across multiple cloud platforms, machine learning capabilities, and geospatial analysis.

Google offers two pricing plans for their managed big data service:

  • The on-demand pricing model comes with a free tier of 1TB per month, after which users are charged according to how much data is scanned at $5 per TB.
  • The fixed-fee model costs users a fixed sum to buy 100 slots for a set time, from one month ($2,000 for 100 slots) to one year ($1,700 for 100 slots).

How to Choose the Right Big Data Tool for Your Needs

With so many Big Data platforms available on the market, there’s now a dedicated tool or platform for virtually every task connected to managing and making sense of Big Data. However, without good preparation, it might be pretty tricky to find the right ones and then learn how you can use those for the best results.

So, before you start reviewing the tools available, it’s crucial to consider the following:

  • your main goals and objectives for using Big Data analytics tools
  • the kind of data you want the platform to process
  • which data processing type would be more useful for you (batch processing or real-time processing, or maybe both?)
  • do you plan to implement machine learning models on top of the big data analytics tool
  • what integrations and features do you need the tool to have
  • any industry-specific features or capabilities that you require the platform to have

Defining your goals and expectations will narrow down your search for a Big Data tool to those most suitable for your needs, rather than having to review each of the hundreds that are available. Plus, doing so lowers the risk of choosing a tool that doesn’t fit your business strategy and thus gives you meaningless results.

The Benefits of Using a Big Data Analytics Tool

Researching and testing those tools might take a bit of time, but once you find ones that match your needs and goals perfectly, you’ll quickly start noticing the enormous benefits of using Big Data in your strategy.

To name just a few of the advantages, you’ll be able to:

  • build a complete customer profile using the collected data and then tailor products and services to their expectations.
  • create personalized marketing and sales campaigns that will match your target audience’s interests or issues
  • optimize existing business processes (be it from manufacturing, supply chain, planning, or basically any process you may want to have better insights into)
  • invent new products or services that will address your audience’s needs
  • visibly improve business operation efficiency by having performance data updated in real-time
  • prevent data loss by having the data automatically replicated on different nodes

As a bonus, using Big Data can also help you significantly cut the costs of running your business. For example, in one survey, organizations that said they were able to use a Big Data strategy with success reported an average 8% increase in revenue and a 10% reduction in costs.

Examples of How Businesses Are Using Big Data Analytics to Improve Their Bottom Line

Finally, let’s look at some examples of how other businesses are already using Big Data effectively in order to demonstrate its full potential:

Amazon

Big Data is the key to Amazon’s powerful recommendation engine and dynamic pricing. As it analyzes data from customers’ searches, clicks, and purchases, it can recommend the right products to each of them instantly. Product prices on Amazon are also automatically adjusted thanks to Big Data every ten minutes, on average.

Marriott Hotels

Analyzing weather forecasts, room availability, demand, the number of cancellations, and upcoming events allows Marriott to adjust the prices of their rooms in real time. Marriott’s other main goal with Big Data is to provide the smoothest and most comfortable experience to its guests by analyzing their behavior and then inventing new services.

For example, using facial recognition, based on a computer vision application, lets guests check-in in a matter of seconds.

Netflix

The streaming platform’s recommendation engine is powered by Big Data as well. Conducting a Big Data analysis of what kind of movies or series Netflix users watch most often enables Netflix to create a fully-personalized recommendation list for each of them.

The results? Netflix had an 83% retention rate in 2021 – the second highest for streaming services after Disney+.

UberEats

Did you know that Uber collects information about how long it usually takes to prepare a specific meal so that they can pinpoint the exact time when the delivery person should come and pick it up for the meal to still be warm upon arrival? Moreover, UberEats gathers and analyzes local metrics about weather forecasts, all to predict how this affects delivery times.

Getting Started with Big Data Analytics

Big Data is no longer just a trend. It’s the future. If you want to bring more people to your business, keep them coming back, and generate more sales – the more you know about them, the better. And Big Data gives you exactly that – plenty of useful information about who your current and future customers are and what they want.

Once you have this information, it becomes easier to tailor your strategy, products, services, and customer support to your customers. Then, you can bet they will keep coming back to you.

But to make sense of the vast amounts of information, you need a robust set of Big Data tools. Those mentioned in this article should be very helpful here. So how about seeing the true power of Big Data with your own eyes?

Big Data Experts at Nexocode

Are you looking to implement big data analytics in your business or organization? If so, consider reaching out to the experts at nexocode. Our team of experienced professionals has the knowledge and expertise to help you successfully implement a big data analytics solution that meets your specific needs and goals. We offer a range of services, including consultation, design, data integration, data science, pipelines development, comprehensive tool implementation, and support, to ensure that your big data analytics project is a success. Contact us today to learn more and to schedule a consultation with one of our experts. We look forward to helping you achieve your big data analytics goals.

About the author

Dorota Owczarek

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

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With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams.
She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

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