#big data tools

Understanding Big Data and Its Growing Importance

Big data refers to extremely large and complex datasets generated from digital platforms, connected devices, transactions, sensors, and online interactions. These datasets are characterized by high volume, velocity, and variety, making them difficult to process using traditional data management systems. As organizations become more data-driven, big data has emerged as a foundational element for uncovering insights, improving efficiency, and enabling smarter decision-making.

In today’s digital environment, data is generated continuously and at unprecedented speed. From social media activity and IoT sensors to enterprise systems and cloud applications, the ability to collect and interpret this information has become a competitive necessity. Big data enables organizations to move beyond intuition and rely on evidence-based strategies supported by advanced data analytics.

The Role of Data Analytics in Big Data Ecosystems

Data analytics is the engine that transforms raw big data into meaningful insights. It involves examining large datasets to identify patterns, trends, correlations, and anomalies that support informed decisions. Modern analytics techniques leverage machine learning, artificial intelligence, and statistical modeling to process data in real time and at scale.

Predictive analytics is one of the most impactful applications of big data analytics. By analyzing historical and real-time data, organizations can forecast outcomes, anticipate risks, and optimize operations. Descriptive and prescriptive analytics further enhance understanding by explaining what happened and recommending actions to improve future results. As analytics capabilities evolve, they are becoming more automated, faster, and easier to integrate into everyday workflows.

Big Data Tools Powering Modern Analytics

The rapid expansion of big data has driven the development of advanced big data tools designed to store, process, and analyze massive datasets efficiently. Cloud-native platforms, distributed computing frameworks, and real-time data processing engines now form the backbone of modern data ecosystems. These tools enable scalability, flexibility, and cost efficiency while supporting both structured and unstructured data.

Real-time and streaming analytics tools are gaining traction as organizations seek instant insights from continuous data flows. Technologies that support event-driven processing allow businesses to respond immediately to changes, whether detecting anomalies, monitoring systems, or personalizing digital experiences. At the same time, data lake and lakehouse architectures are simplifying how data is stored and accessed across different platforms.

AI, Automation, and the Evolution of Big Data

Artificial intelligence has become deeply intertwined with big data. Machine learning models rely on large datasets to learn patterns and improve accuracy over time. As a result, big data and AI now function as complementary technologies, driving innovation across analytics, automation, and intelligent decision-making.

Automation is also reshaping how data pipelines are built and managed. Automated data ingestion, cleansing, and quality monitoring reduce manual effort and improve reliability. These advancements allow data teams to focus more on insight generation rather than infrastructure management. The integration of AI into analytics platforms is further simplifying how users interact with big data through natural language queries and automated insights.

The expanding adoption of big data solutions is reflected in long-term growth projections. Global big data-related activity is expected to expand at a CAGR of 14.9% from 2024 to 2030, highlighting the accelerating reliance on data-driven technologies across digital ecosystems. This sustained growth underscores the increasing value placed on analytics-driven decision support and scalable data platforms.

Cloud, Edge, and Real-Time Big Data Processing

Cloud computing has become central to big data strategies, offering elastic storage and computing resources without heavy infrastructure investments. Cloud-based big data tools allow organizations to process massive datasets efficiently while adapting to changing workloads. Hybrid and multi-cloud environments further enhance flexibility and resilience.

At the same time, edge computing is gaining importance as data is increasingly generated closer to devices and sensors. Processing data at the edge reduces latency and bandwidth usage, enabling faster responses for time-sensitive applications. Combined with real-time analytics, this approach supports use cases such as monitoring, predictive maintenance, and intelligent automation.

Governance, Privacy, and the Future of Big Data

As big data volumes grow, governance and data privacy have become critical priorities. Organizations are implementing stronger frameworks to ensure data quality, security, and ethical use. Privacy-preserving analytics, transparent data practices, and compliance-driven architectures are shaping the future of big data management.

In today’s digital world, where data has become an integral part of our daily lives. May it be our phone’s microphone, websites, mobile applications, social media, customer feedback, or terms & conditions – we consistently provide “yes” consents, so there is no denying that each individual's data is collected and further pushed to play a bigger role into the decision-making pipeline of the organizations.

This collected data is extracted from different sources, transformed to be used for analytical purposes, and loaded in another location for storage. There are several tools present in the market that could be used for data manipulation. In the next sections, we will delve into some of the top tools used in the market and dissect the information to understand the dynamics of this subject.

Architecture Overview

While researching for top tools, here are a few names that made it to the top of my list – Snowflake, Apache Kafka, Apache Airflow, Tableau, Databricks, Redshift, Bigquery, etc. Let’s dive into their architecture in the following sections:

Snowflake

There are several big data tools in the market serving warehousing purposes for storing structured data and acting as a central repository of preprocessed data for analytics and business intelligence. Snowflake is one of the warehouse solutions. What makes Snowflake different from other solutions is that it is a truly self-managed service, with no hardware requirements and it runs completely on cloud infrastructure making it a go-to for the new Cloud era. Snowflake uses virtual computing instances and a storage service for its computing needs. Understanding the tools' architecture will help us utilize it more efficiently so let’s have a detailed look at the following pointers:

Image credits: Snowflake

Now let’s understand what each layer is responsible for. The Cloud service layer deals with authentication and access control, security, infrastructure management, metadata, and optimizer manager. It is responsible for managing all these features throughout the tool. Query processing is the compute layer where the actual query computation happens and where the cloud compute resources are utilized. Database storage acts as a storage layer for storing the data. 

Considering the fact that there are a plethora of big data tools, we don’t shed significant light on the Apache toolkit, this won’t be justice done to their contribution. We all are familiar with Apache tools being widely used in the Data world, so moving on to our next tool Apache Kafka.

Apache Kafka

Apache Kafka deserves an article in itself due to its prominent usage in the industry. It is a distributed data streaming platform that is based on a publish-subscribe messaging system. Let’s check out Kafka components – Producer and Consumer. Producer is any system that produces messages or events in the form of data for further processing for example web-click data, producing orders in e-commerce, System Logs, etc. Next comes the consumer, consumer is any system that consumes data for example Real-time analytics dashboard, consuming orders in an inventory service, etc.

A broker is an intermediate entity that helps in message exchange between consumer and producer, further brokers have divisions as topic and partition. A topic is a common heading given to represent a similar type of data. There can be multiple topics in a cluster. Partition is part of a topic. Partition is data divided into small sub-parts inside the broker and every partition has an offset.

Another important element in Kafka is the ZooKeeper. A ZooKeeper acts as a cluster management system in Kafka. It is used to store information about the Kafka cluster and details of the consumers. It manages brokers by maintaining a list of consumers. Also, a ZooKeeper is responsible for choosing a leader for the partitions. If any changes like a broker die, new topics, etc., occur, the ZooKeeper sends notifications to Apache Kafka. Zookeeper has a master-slave that handles all the writes, and the rest of the servers are the followers who handle all the reads.

In recent versions of Kafka, it can be used and implemented without Zookeeper too. Furthermore, Apache introduced Kraft which allows Kafka to manage metadata internally without the need for Zookeeper using raft protocol.

Image credits: Emre Akin

Moving on to the next tool on our list, this is another very popular tool from the Apache toolkit, which we will discuss in the next section.

Apache Airflow

Airflow is a workflow management system that is used to author, schedule, orchestrate, and manage data pipelines and workflows. Airflow organizes your workflows as Directed Acyclic Graph (DAG) which contains individual pieces called tasks. The DAG specifies dependencies between task execution and task describing the actual action that needs to be performed in the task for example fetching data from source, transformations, etc.

Airflow has four main components scheduler, DAG file structure, metadata database, and web server. A scheduler is responsible for triggering the task and also submitting the tasks to the executor to run. A web server is a friendly user interface designed to monitor the workflows that let you trigger and debug the behavior of DAGs and tasks, then we have a DAG file structure that is read by the scheduler for extracting information about what task to execute and when to execute them. A metadata database is used to store the state of workflow and tasks. In summary, A workflow is an entire sequence of tasks and DAG with dependencies defined within airflow, a DAG is the actual data structure used to represent tasks. A task represents a single unit of DAG.

As we received brief insights into the top three prominent tools used by the data world, now let’s try to connect the dots and explore the Data story.

Connecting the dots 

To understand the data story, we will be taking the example of a use case implemented at Cubera. Cubera is a big data company based in the USA, India, and UAE. The company is creating a Datalake for data repository to be used for analytical purposes from zero-party data sources as directly from data owners. On an average 100 MB of data per day is sourced from various data sources such as mobile phones, browser extensions, host routers, location data both structured and unstructured, etc. Below is the architecture view of the use case.

Image credits: Cubera

A node js server is built to collect data streams and pass them to the s3 bucket for storage purposes hourly. While the airflow job is to collect data from the s3 bucket and load it further into Snowflake. However, the above architecture was not cost-efficient due to the following reasons:

AWS S3 storage cost (for each hour, typically 1 million files are stored).

Usage costs for ETL running in MWAA (AWS environment).

The managed instance of Apache Airflow (MWAA).

Snowflake warehouse cost.

The data is not real-time, being a drawback.

The risk of back-filling from a sync-point or a failure point in the Apache airflow job functioning.

The idea is to replace this expensive approach with the most suitable one, here we are replacing s3 as a storage option by constructing a data pipeline using Airflow through Kafka to directly dump data to Snowflake. The following is a newfound approach, as Kafka works on the consumer-producer model, snowflake works as a consumer here. The message gets queued on the Kafka topic from the sourcing server. The Kafka for Snowflake connector subscribes to one or more Kafka topics based on the configuration information provided via the Kafka configuration file.

Image credits: Cubera

With around 400 million profile data directly sourced from individual data owners from their personal to household devices as Zero-party data, 2nd Party data from various app partnerships, Cubera Data Lake is continually being refined.

Conclusion

With so many tools available in the market, choosing the right tool is a task. A lot of factors should be taken into consideration before making the right decision, these are some of the factors that will help you in the decision-making – Understanding the data characteristics like what is the volume of data, what type of data we are dealing with - such as structured, unstructured, etc. Anticipating the performance and scalability needs, budget, integration requirements, security, etc.

This is a tedious process and no single tool can fulfill all your data requirements but their desired functionalities can make you lean towards them. As noted earlier, in the above use case budget was a constraint so we moved from the s3 bucket to creating a data pipeline in Airflow. There is no wrong or right answer to which tool is best suited. If we ask the right questions, the tool should give you all the answers.     

So, let’s talk about big data. If you’ve been in the tech world for any amount of time, you know how crucial data has become. It’s not just numbers and charts anymore—data is the backbone of every decision we make in business, from marketing strategies to operations and everything in between. But with so much data flowing in and out, how do you make sense of it all? Well, that’s where the right big data tools come into play.

We’ve all faced that overwhelming moment when we’re staring at huge datasets, wondering, “Where do I even start?” Don’t worry, you’re not alone. With the right tools, you can easily harness the power of data and transform it into actionable insights. But here’s the kicker: there are so many options out there, it’s hard to know which ones are truly worth your time.

Imagine having access to the best big data tools that not only help you analyze complex data but also simplify the process so you can make smarter, faster decisions. Sounds like a dream, right? Well, we’ve got some good news for you. We’ve done the research and rounded up the top 5 big data tools for 2025. These tools aren’t just powerful; they’re also user-friendly, ensuring that you can get the most out of your data without needing to be a data scientist.

From tools that help you visualize data more effectively to those that make predictive analytics a breeze, we’ve covered everything you need to stay ahead in the fast-paced world of big data. But here’s the catch—this is just a sneak peek. To get the full scoop on these game-changing tools and how they can elevate your data game, make sure to check out our detailed list.

Top 30 Data Analytics Tools

Data is the new oil. It has become a treasured commodity today for data analytics and has taken on a serious status. With the daily growing data volume, it is now at a scale that no human can deal with the amount manually. Businesses worldwide have found growth in their organizations by incorporating data analytics into their existing technology platforms.

The concept of data analytics has evolved over time and will continue to rise. Data analytics has become an important part of managing a business today, where every business owner wants their business to grow and increase its revenue in order to maintain a competitive edge in this ever-changing marketplace, they need to be able to use data effectively.

What is Data Analytics?

Data analytics is the science of studying raw data with the intent of drawing conclusions from it. It is used in multiple industries to allow companies and organizations to make more promising data-driven business decisions.

Data analytics covers an entire spectrum of data usage, from collection to analysis to reporting. Understanding the process of data analytics is the ultimate power and it will be the future of almost every industry.

There are multiple types of data analytics including descriptive, diagnostic, predictive, and prescriptive analytics.

Let’s learn about the different types of data analytics in detail.

‍Types of Data Analytics:

Descriptive Data Analytics:

Descriptive data analytics is the process of examining data to summarize what is actually happening. It provides a basic understanding of how the business operates and helps to identify which factors are affecting the business and which aren't. It supports the exploration and discovery of insights from your existing data and based on that provides a basic understanding of your business.

Diagnostic Data Analytics:

Diagnostic Data Analytics is used to diagnose any business problems. It generally answers the question: why did it happen? Data can be examined manually, or used by an automated system to generate a warning. Diagnostic data analytics is an advanced analytical approach that is used to find the cause of the problem faced by a business.

Predictive Data Analytics:

Predictive data analytics is a form of analytics that uses both new and historical data to forecast activities, behavior, and trends. It is used to analyze current data to make predictions about future events. One important use case for predictive analysis is to help retailers understand customer buying patterns and optimize inventory levels to maximize revenues.

Prescriptive Data Analytics:

Prescriptive data analytics is the last level of analytics that is performed on the outcome of other types of analytics. It is the process of defining an action based on descriptive and predictive data analytics results. In this stage, different scenarios are analyzed to determine how each scenario is likely to play out given past data. This can help businesses know what action to take for a good outcome.

These four types of data analysis techniques can help you find hidden patterns in your data and make sense of it. All these types of data analytics are important in other ways and can be used in different business scenarios.

Importance of Data Analytics:

Data analytics is extremely important for any enterprise and has become a crucial part of every organization's strategy in the past decade. The reason for this is simple: Big data has opened up a world of opportunities for businesses. Data analysts have become essential in helping companies process their huge sets of data for making meaningful decisions.

The benefits offered by analyzing data are numerous some of them are mentioned below:

It helps businesses to determine hidden trends and patterns.

Improves efficiency and productivity of the business by helping them to take data-driven decisions.

Identifies weaknesses and strengths in the current approach.

Enhances decision-making, which helps businesses to boost their revenue and helps solve business problems.

It helps to perform customer behavior analysis accurately to increase customer satisfaction

Data analytics lets you know what is working and what can improve. According to experts, the lack of data analysis and usage can result in failed business strategies and also cause loss of customers. So in order to take your business to the next level, one must always adopt data analytics techniques and should be familiar with the steps involved in it.

Data Analysis Process: Steps involved in Data Analytics

Steps in data analytics are a set of actions that can be performed to create useful and functional data. In this section, we will detail the stages involved in data analytics.

Understanding Business Requirements

One of the most important factors behind successful data analysis is a proper understanding of the business requirements. An analyst needs to have a clear idea about what kind of problem the business is facing and what can be done to overcome the problem. The other important task is to understand what type of data needs to be collected to solve the given problem.

Collecting Data

When it comes to data analytics, it is very important that the right kind of data is collected. After understanding the business problem the analyst should be aware of the type of data to be collected to solve the problem. Data can be collected in many ways, including survey forms, interviews, market research, web crawlers, log files, event log files, and even through social media monitoring apps.

Data wrangling

In data wrangling, data is cleaned and managed so that it can be utilized in order to perform data analysis. This process can involve converting data from one format to another, filtering out invalid or incorrect data, and transforming data so that it can be more easily analyzed. Data wrangling is an important step in data analysis because it can help ensure that the data used in the analysis is of high quality and is in a suitable format.

There are many steps involved in data wrangling, including

1. Gathering data from a variety of sources.

2. Cleaning and standardizing the data.

3. Exploring the data to identify patterns and relationships.

4. Transforming the data into a format that can be used for different tasks.

5. Saving the wrangled data in a format that can be easily accessed and used in the future.

The steps involved in data wrangling can vary depending on the type and format of data you are working with, but the final goal is always the same, to transform raw data into a format that is more useful for performing accurate analysis.

Exploratory Data Analysis (EDA):

Exploratory Data Analysis (EDA) is a statistical approach used to achieve insights into data by summarizing its major features. This procedure is used to comprehend the data’s distribution, outliers, trends, and other factors. EDA can be used to select the best-fitting statistical models and input variables for a dataset.

A typical EDA process might begin with a series of questions, such as

What are the primary components of the dataset?

What are the most significant variables?

Are there any outliers or unusual observations or behaviors?

After asking these basic questions, the analyst should then investigate the data visually, using charts such as histograms, scatter plots, and box plots. These visual methods can help to identify features such as trends, and unusual observations. This process of EDA can help to reveal important insights into the data, and can be used to guide further analysis.

EDA can provide insights that may not be obvious from merely looking at the data itself. Overall, it is an essential tool for data analysis and should be used whenever possible.

Communicating Results:

Communicating results is the final and the most vital aspect of the data analysis life cycle because it allows others to understand the conclusions of the analysis. Results also need to be communicated in a clear and concise way so they can be easily understood by people with less technical acumen as well. Additionally, conveying results allows for feedback and discussion to improve the quality of the findings during the analysis procedure.

The data analytics life cycle generally goes through these five-step procedures that help to find precise conclusions. But apart from the benefits, some challenges are faced during the data analytics process.

Overall Challenges in Data Analysis:

There can be many types of challenges encountered during the data analysis journey but the two most common challenges are mentioned below:

Data issues

Data analysis-related issues.

1. Data Issues:

Data-related problems are one such type of issue encountered during the data analysis journey. Some data-related issues are mentioned below:

Incorrect or inaccurate data

Incomplete data

Data that is not timely ingested

Unorganized data

Irrelevant data

Data integration issues

Handling large datasets

The data team needs to guarantee to provide the correct data and a good and reliable data integration platform should be preferred to ensure correct and timely ingestion of data. A proper ETL tool that provides safe and secure data storage should be selected.

2. Data Analysis Related Issues:

The data analysis process can be challenging if the data is not well-organized, some challenges are mentioned below:

Absence of skills to interpret data.

Data cleaning and preparation can be very time-consuming.

Choosing the right statistical method can be a challenge.

The results of the analysis can be misinterpreted.

Communicating the results in a simpler way can be tough

To overcome these challenges businesses should use low-code data analytics platforms that will help to save manpower and thus reduce costs. With careful planning and execution, one can easily perform analysis without any hassles. By using the right tools and techniques, businesses can overcome these challenges and make better data-driven decisions.

Need for Data Analysis Tools:

In a world where data is continuously being generated, it is becoming hard to make sense of it all without the help of data analysis tools.

There are many reasons why we need data analysis tools. They help us to process, understand, and make use of data effectively. Data analysis tools help us to see patterns and trends in data without actually coding. Nowadays, businesses don't need a highly skilled person to perform the data analysis process in fact they can perform the analysis on their own because of the tools present in the market.

The data analysis tools in the market can also help to enhance communication and collaboration within your organization through alerts and email functionalities. In some cases, they can also help to automate decision-making processes.

Criteria For Choosing the Right Data Analysis Tool:

There is a wide variety of data analysis tools available in the market but the best-fitted tool for you will depend on the specific data set and the desired business outcome. When choosing a data analysis tool, it is essential to assess the specific features and capabilities of the tool, and the user’s needs should also be considered. For example, if you are looking to perform complex statistical analysis, then a statistical software package would be the best choice. On the other hand, if you are looking to create interactive dashboards, then a no-code data analytics platform would be a more suitable fit.

Below listed are some criteria that one should consider before choosing the right data analytics platform according to the requirements.

1. No-code Data Analytics Platform:

No-code data analytics platforms equip users with the capability to quickly analyze data with ease without having to write even a single line of code. This can save users a lot of time and effort by making data analysis more streamlined.

Some benefits provided by such data platforms are mentioned below:

No technical skills required: Analysis of data on these types of platforms can be performed by users of all skill types and different experience levels. Data analysis is made more accessible to individuals which allows them to benefit from it.

Supports Different Data types: Wide variety of data can be analyzed be it structured or unstructured, which makes these platforms more versatile.

Easy Integration: Easy integration with different sources is one of the best features provided by no-code data platforms.

Flexible pricing plans: No-code platforms provide scalability and are proven to be very cost-effective. This feature makes them useful for businesses of all sizes and stature.

If you are looking for a good and reliable no-code data analytics platform that has all these features then Sprinkle Data is the best option.

     2. Availability of Different Types of Charts:

Charts can help to picture data, and spot trends and patterns effortlessly. They help to make intricate data more coherent and can help individuals to make better decisions. Charts used with proper statistical techniques can be useful in making predictions about future behavior as well. They also can be used to interpret and find relationships between different variables and are useful in finding outliers in data. Different types of charts can be used to perform accurate analysis, some important chart types include:

Bar/column charts are one of the most typically used chart types and are especially helpful in comparing data points.

Line charts are used for depicting changes over time.

Pie charts are helpful in determining proportions across various categories

Scatter plots are useful for visualizing relationships between two numerical data points and are primarily used to identify trends and outliers in data.

Histograms are used to give information about the data distribution.

An area chart is based on a line chart and is primarily used to depict quantitative data by covering the area below the line.

Combo Chart is a combination of a line and a bar chart that depicts trends over time.

Funnel charts help to portray linear processes with sequential or interconnected phases in the analysis.

A map is a geographical chart type used to visualize data point density across different locations.

A stacked bar chart is a form of bar chart depicting comparisons of different data categories.

Charts are an integral part of any data analytics tool and can add meaning to the analysis. They help to communicate the conclusions of the analysis in a concise manner. So always choose a data analysis tool that has these charts with additional attributes like labels, a benchmark value, and different colors to easily differentiate.

All the chart types mentioned above are available in the Sprinkle Data analytics tool accessible with just a single click.

    3. Dashboard With a Good Visual Interface

A dashboard is a visual user interface that provides easy access to key metrics and consists of a sequence of charts, tables, and other visual elements that can be customized and systematized to provide insights into specific datasets with advantages like delivering visibility into an organization's performance in real time.

The key features that a dashboard should contain are mentioned below:

Interactivity: Dashboards with good interactivity permit users to filter and drill down into data for more detailed analysis.

Easily Editable layout: Customized dashboard show only the data that is relevant to the analysis.

Easy to share: Dashboards that can be easily shared with others to explore and analyze the data.

Less Runtime: A data analytics platform whose Dashboards take less time to run should be picked.

Monitoring: In case of a dashboard failure proper email alerts should be provided to the user with the reason for the error.

User-Friendly Interface: A dashboard with a user-friendly interface like drag and drop functionality is easy to use.

Live Dashboard: If you need to track data in real-time a live dashboard is the best option for your business.

If you are confused about which data analytics platform should be preferred to get all these features then you should prefer Sprinkle Data.

The best dashboard for your needs is the one that must follow all these criteria and will depend on the type of data you need to track, and the level of detail you need to acquire.

    4. Cost Efficient:

A cost-effective data analytics platform helps to save money on software and hardware. These tools can help organizations save money in a number of ways. By enabling organizations to understand their data better, these tools can help to recognize zones where costs can be decreased. Moreover, a platform with flexible and scalable pricing plans should be adopted to pay a reasonable price according to the requirements.

Sprinkle Data has a flexible pricing plan that is fully customizable according to the needs of users enabling them to save costs while performing high-level analytics.

Ultimately, the best way to choose the right data analysis tool is to consult with experts in the field and try different tools to see which one works best for your specific needs.