TOP Tools for Big Data Analytics in 2022
Big data and analytics are two concepts that are on nearly every business agenda, especially coupled together. Nowadays, analytics projects that use big data or advanced analytics are becoming wildly popular because of the many benefits that come from using the two together, especially the ability to make business decisions smarter and more accurate.
Business decisions need to have a measurable effect, direct or indirect, but overall, and an effect. For example, customer profitability helps organizations segment customers and prioritize them for retention, resulting in a higher number of valuable customers and higher average customer profitability.
This chain of events follows the concept that metrics need to align with organizational priorities, which is why linking analytic outputs to measurable outcomes results in better complexity management, mapping processes, and overall better business results. In that sweet spot, we find big data and analytics. Big data is automatically thought of as being a default companion of advanced analytics, but that’s not always the case. The data-crunching power needed to manage big data characteristics such as volume, velocity, and variety do not necessarily require sophisticated algorithmic processing.
Instead, you have to think about the complexity of the analytical question you’re trying to answer to get to a desired organizational priority. If the analytical question is complex enough, there’s a valid need for advanced analytics tools that are specialized.
Success with big data and analytics requires new and innovative forms of information processing such as unconventional data management technologies, architectures, and analytics capabilities. Organizations must develop a big data analytics strategy for the new economics of information.
When COVID-19 hit, many organizations were faced with the realization that traditional analytics techniques that rely on large amounts of historical data are no longer relevant. Uncertainties and destabilizing events have rendered plenty of data virtually useless, which is why organizations are now pivoting to AI big data analytics techniques that rely on less, more varied data.
In this blog, we’ll cover the top tools for big data analytics that you can leverage in 2022 to help organizations and society navigate disruptive change, uncertainty, and the opportunities lying beneath the surface.
Let’s get going!
What is Big Data Analytics?
To meet modern business demands, organizations need to go beyond traditional big data analytics, especially considering information’s unique economic characteristics that bring real value to businesses. All who are looking to accelerate their digital transformation journey and aspirations need to consider how to make smart, connected, and contextual decisions.
According to SAP, big data analytics “examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions.”
Within that context, we focus on two of the biggest benefits of big data analytics: harness data to identify new opportunities and the speed it brings to make immediate, better-informed decisions that enable organizations to work faster and stay agile.
Big data advanced analytics uses sophisticated quantitative methods to extract insights that would otherwise be hard to discover with traditional approaches. Advanced analytics covers predictive, prescriptive, and artificial intelligence techniques such as machine learning and natural language processing.
Tools for analyzing Big Data
We are increasingly seeing progressive organizations complement the best of human decision-making capabilities with computing prowess. intelligence, and accuracy of artificial intelligence in the form of analytics tools for big data and tools for analyzing big data to discover or create opportunities.
Next, we’ll dive into some of the most interesting big data analytical tools for your business:
Big data analytics tool that integrates, processes, and prepares data for analytics in the cloud. Integrate.io gathers all data sources in a centralized location, offering an intuitive UI that helps users implement ETL, ELT, or a replication solution.
The Integrate.io toolkit creates data pipelines with low-code and no-code capabilities, helping users make the most out of their data without having to invest in hardware, software, or specialized professionals.
Integrate.io is favored by users who look for a scalable and elastic cloud platform for big data with immediate connectivity and a rich set of out-of-the-box data transformation elements.
Open-source software framework and filesystem that is distributed, scalable, and portable. Apache Hadoop employs a clustered filesystem to be able to process datasets of big data by using the MapReduce programming model.
Apache Hadoop is completely written in Java, providing cross-platform support, cementing its place as one of the most popular big data analytic tools out there. In fact, more than half of Fortune 500 companies favor Hadoop for their big data endeavors, including the likes of Amazon, IBM, Intel, Microsoft, Facebook, and more.
What makes Apache Hadoop so special is its Hadoop Distributed File System which can hold all types of data in the same file system, from video, images, and plain text, to JSON or XML formats, to name a few. Another benefit of Apache Hadoop is the fact that it's highly scalable and highly available as it rests on a cluster of computers.
Data modeling tool that offers column indexes with the superior performance of log-structured updates, strong support for denormalization and materialized views, as well as built-in caching. Also a part of the Apache family, Cassandra is cost-free and open-source, offering distributed NoSQL database management systems built to process massive volumes of data across multiple servers.
Users favor Cassandra as it's known for delivering high availability by employing the Cassandra Structure Language to interact with the database. Some big names that use Cassandra include Accenture, American Express, General Electric, Honeywell, and more.
Another key aspects of Cassandra are its automated replication, linear scalability, and simple ring architecture.
Designed to help users make data-driven decisions with confidence, Tableau helps answer complex questions quickly and unearth actionable insights more frequently. Tableau connects directly to hundreds of data sources, regardless if they are on-premises or in the cloud, ensuring users have it easy when starting analytics tasks.
Thanks to interactive dashboards, drag-and-drop functionality, and natural language queries, Tableau is one of the most sought-after big data analytics tools to visualize and understand data. The Tableau software comes with Tableau Desktop, Tableau Server, ad Tableau Online, with the recent additions of Tableau Public and Tableau Reader.
All in all, Tableau can handle all data sizes and it’s easy to use by technical and non-technical folks thanks to real-time customized dashboards that are easy to follow. Mainly, Tableau has made a name for itself as the go-to tool for data visualization and exploration.
Other worthy mentions. We would be remiss if we failed to mention other amazing big data analytics tools like Adverity, Dataddo, CDH, Datawrapper, Storm, Talend, RapidMiner, and many more.
Technology for big data
Big data analytics, when used in business scenarios that require it, leads to smarter business moves, improved operations, and higher profits. Most organizations are sitting on piles of big data, so the key is to find the right tools to harness and extract insights and value out of it.
Volume, velocity, and variety are big data characteristics that are the very definition of big data. There are moves Vs nowadays which include value and veracity.
- Volume refers to the number of data points.
- Velocity is how quickly data is generated and how quickly it moves.
- Variety is structured and unstructured data coming in from different sources, thus creating that diversity of data types.
Some of the best tools leverage different and advanced forms of technology for big data to reach the goal of extracting the most value out of data including cloud computing, data management, data mining, data lakes, data warehouses, in-memory analytics, machine learning, predictive analytics, text mining, visual text analytics, and more.
Smart machines are capable of completing a set of tasks that typically demand human intelligence. Artificial intelligence easily and seamlessly integrates with big data thanks to its ability to work with data analytics via machine learning or deep learning to pull from every data point and use inputs to generate desired outcomes or outputs. In short, artificial intelligence adds considerable value to big data analytics by extracting higher quality insights from big data by learning and adapting over time without specific instructions or programmed code. After all, data is what teaches artificial intelligence technologies like machine learning how to behave under specific scenarios.
- Data lakes.
According to Gartner, data lakes “collect unrefined data (in its native form, with limited transformation and quality assurance and intrinsic governance) and allow users to explore and analyze it in a highly interactive way. Data lakes don’t replace data warehouses or other systems of record; rather, they complement them by storing unrefined data that may hold great value. The sweet spot for data lakes is the world of pure discovery, data science, and iterative innovation.” Acting as a central storage repository, data lakes hold big data from multiple sources in their raw format, meaning data is kept in a more flexible setting for future use.
- Predictive analytics.
This technology of big data typically works with probabilities and is mostly used to predict outcomes over a specified period of time, also known as forecasting, or used to highlight unknowns related to multiple outcomes, also known as simulation. Predictive analytics helps us understand what to expect and what’s likely to happen so organizations are better prepared for future scenarios. Traditionally, predictive analytics relies on techniques like predictive modeling, forecasting, regression analysis, pattern discovery, pattern matching, multivariate statistics, machine learning, and more.
- Prescriptive analytics.
Technology is designed to calculate the best path available to get or influence a specific outcome so users can take action accordingly. Prescribe analytics coupled with predictive analytics helps address questions like what can be done¡ or what can be done to make sure a specific outcome happens. Prescriptive analytics has rule-based approaches to incorporate knowledge in a structured format and optimization techniques to look for optimal outcomes to build strategic plans of action. Traditionally, prescriptive analytics relies on techniques like complex-event processing, graph analysis, simulation, and recommendation engines, to name a few.
- In-memory database.
Based on a database management system, in-memory databases store data collections directly in the working memory of computers. Using RAM offers strategic advantages for in-memory databases, with the most prominent one being faster access and processing speeds as it omits the requirements to gain access to disks. On the downside, one of the risks of in-memory databases is that by collecting and controlling all data in the main memory, there’s a higher probability of losing data in the case of a server failure.
Platform for big data
Now that we’ve covered some of the most popular big data analytics tools and technologies, let’s talk about platforms. Platforms are essential to big data analytics because they offer a one-stop-shop for everything you could possibly need that is big data related. Big data platforms encompass data collection, analysis, preparation, and reporting tools with efficient scalability.
Let’s explore some of the most prominent platforms for big data.
Known as the hybrid data cloud company by excellence, Cloudera offers a hybrid cloud platform for any type of data, in any place, from the Edge to AI.
Cloudera is a multi-environment analytics platform that is powered by open-source technologies. The platform puts data management at the fingertips of analysts thanks to its enterprise-grade data cloud that offers scalability and elasticity to manage massive data workloads.
Key differentiators of Cloudera are:
- Data-driven insights derived from the platform’s data discovery, analysis, and interpretation tools.
- Industrialized AI approach to BI that automates machine learning and artificial intelligence processes, making them repeatable and predictable, and as such, reducing the time it takes to go from raw data to results.
- No more silos. Cloudera moves away from costly and ineffective silos with its unified platform that performs multiple data analysis tasks at once, right at the source
- Consistent safety protocols that leverage encryption and granular policies across the platform. Built-in auding and lineage tracking capabilities are also a plus.
From raw data to insights, Microsoft Azure HDInsight stores data, regardless if it is structured, semi-structured, unstructured, or streaming, from inside and outside organizations.
Key differentiators of Microsoft Azure HDInsight are:
- Data storage of all types and sizes at an affordable price.
- Hadoop with big data access minus the headaches as it gives customers the ability to deploy a full Apache-based Hadoop cluster on HDInsight in a matter of minutes.
- New insights thanks to the combined use of relational and non-relational data with PolyBase.
- Easy Hadoop data analysis right inside Microsoft Excel.
- Use of PowerBI to collect both internal and external data.
Market intelligence and enterprise analytics platform that delivers powerful insights by unearthing trends and insights using a single, cohesive, and integration-friendly system that’s democratic, flexible, scalable, and super fast. On top of producing critical insights, 1010data provides brand performance metrics, where data scientists and statisticians make use of the platform’s seamless integration with R to view query data tables.
1010data leverages the QuickApps framework to trans-consumer trends and brand performance, helping users improve their product’s positioning in the market.
Key differentiators of 1010data are:
- Track consumer trends to find out how they search for and assess products before making a purchase.
- Analyze buyer behavior to boost retention and loyalty rates by looking at shopping cycles.
- Take a look at competitors to identify disruptors, justify product categories, and analyze merchandising strategies.
- Evaluate consumer behavior at the brand, merchant, category, and product-level to maximize ROI.
- Educate investment decisions by using granular insights that result from tracking consumer spending, basket size, and transactions.
Owned by VMWare since 2019, the Pivotal Big Data Suite platform, now referred to as VMWare Tanzu, is an integrated solution that is packed with Greenplum, a data warehouse, GemFire, an in-memory data grid, and Postgres, to help deploy clusters of the PostgreSQL database.
Pivotal has an architecture designed for batch and streaming analytics and is capable of being deployed on-premises or in the cloud.
Key differentiators of Pivotal are:
- Alignment with the PostgreSQL community with open-source contributions that share the same database core.
- Easy to deploy with Kubernetes with an automation layer. Pivotal is available on AWS, Azure, and Google Cloud Platform.
- Deeper data insights using machine learning, graphic, text, and statistical methods, deep learning, and more, all in a single MMP database.
- Geospatial analytics based on postGIS and text analytics.
- Support of R and Python analytical libraries, as well as Tensorflow and Keras.
- Ability to scale up or down and in or out of on-premises or cloud environments.
Visualize big data
As stated earlier, one of the key characteristics of big data is volume. With 2.5 quintillion bytes of data generated every day, it’s no wonder how critical it is to make correlations between such massive amounts of data and visual representations.
It’s one thing to extract insights but what good can insights do if no one really understands them? The visual aspect of presenting big data outcomes in a simple, engaging, and straightforward way helps stakeholders understand processes better and take decisions faster.
Visualization is one of the most critical players in big data analytics techniques. Visualizing big data is a means to make sense of the data and present it in such a way that it’s easy to understand by stakeholders and ultimately, take action. Visualization of big data can be made with charts, maps, interactive content, motion graphics, regression lines, infographics, timelines, or scatter plots, to name a few.
Visualization in big data relies on tools to interpret raw data and process it to create visuals that help us mere mortals make sense of it all. Whether they are numbers, linear algebra, statistical algorithms, trigonometric functions, reports analytics, and many more, they all help simplify data to make it understandable and easy to interpret.
Visualization techniques for big data are crucial to:
- Empower decision-makers to understand data quickly.
- Capture trends.
- Unearth patterns by identifying data relationships and connections.
- Communicate insights to others.
The most prominent visualization types to provide qualitative understanding are:
- Charts. Charts use elements to compare components and show the relationship between different data points. Some of the most common charts are line charts, pie and donut charts, and bar charts.
- Plots. Data visualization aid to present data sets in 2D and 3D. Plots can be scattered plots (X-Y), bubble plots, and histogram plots.
- Maps. Visuals that position different data points on different areas, layouts, etc. They can be distribution maps or heat maps.
- Network diagrams. Node relationships that map one element to another. For example, the ties in social network analysis or sales across a specific geographic region.
Big data engineering services
Best looked at by the roles they play and services they deliver, let’s start with a simple distinction between what a data analyst and a data engineer do. Data analysts use static modeling techniques to summarize data via descriptive analysis. On the other hand, data engineers are in charge of developing and maintaining data pipelines.
Big data engineering services provide a breadth of offerings including the design, build, testing, and maintenance of complex data processing systems that excel at working with large data sets.
Some of the most prevalent data engineering services include:
- Data quality checks
- Data standardization services
- Data discovery
- Data maturity assessments
- Cloud-based solutions for large data volumes
- Real-time and batch data processing
- Database optimization
- Data warehouses
- Advanced big data analytics
- Data streaming development
Now, let’s categorize these services under three umbrellas: data architecture, data processing, and data analytics services.
Design cloud solutions to prepare and automate processes for multi-structured data. Data architecture services look at database and data warehouse optimization and prepare software solutions for easy integration with other systems.
Data-driven solution implementation with real-time and batch data pipelines and processing. The data processing services oftentimes deal with data quality and standardization services, as well as data integration, maintenance, data lakes, and the set up of data warehouses.
Data analytics services consult and design plans for analytical development. These service offerings prepare recommendation systems, search for patterns in data, and evaluate the quality of an organization’s analytical products.
Across these services, the benefits that lie within are meaningful and targeted at helping organizations make business decisions on the fly and with high accuracy.
Here are some of the benefits you can leverage by employing data engineering services:
- Make better decisions based on data insights
- Improve quality and user experience
- Identify new business opportunities and prepare for potential risks
- Reduce costs by simplifying data structures
- Shorten the duration of projects by accessing insights quickly
Big data analytics is dutifully focused on extracting meaningful information from large sets of data. While this is an oversimplified explanation of everything that goes behind the scenes to actually extract insights, the gist of it all is that it’s much more beneficial for companies to unlock the power of the data they’re sitting on than let it rest there unused. Structured, semi-structured, unstructured…data comes in all shapes, sizes, and at different speeds. Without the right tools in big data to help you make sense of it, it can be dizzying to attempt to analyze data and expect relevant insights for relevant use cases. As the COVID-19 pandemic made evident, it’s imperative that organizations are future-ready to swiftly adjust and adapt to uncertainties, and making data-driven decisions quickly can be a game-changer.
Having worked with a bouquet of diverse big data analytics projects for our clients, we possess rich knowledge of the best tools, technologies, methods, platforms, visualization, and engineering services that make up a successful big data project. Across different industries like healthcare, finance, retail, IT, marketing, and more, there’s no barrier to what big data can achieve with the right tools in place. If you’d like to get an assessment or learn more about our big data service offerings, please reach out to Svitla Systems representatives and we’ll be happy to share all details.
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