8 Top Data Analytics Tools (2024)

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If you’re still analyzing data sets in Microsoft Excel, it’s time to learn how a decent analysis tool may simplify your work and help you discover greater business insights.

The eight data analysis tools on our list are the greatest solutions currently available, whether you’re seeking a new data management tool or a powerful engine for training machine learning models.

1. Qlik Sense

Qlik Sense is a data visualization and analysis tool powered by artificial intelligence (AI).

The platform is available to all users in the organization, including those who are not familiar with machine learning or data analysis.

The AI built into Qlik Sense indexes your business analytics and responds to natural language input for quick analysis. This makes it ideal for allowing all users in your company to access valuable information as needed.

However, technical users can still use Qlik Sense to perform deeper, more powerful data analysis, including:

  • Get instant calculations by specifying data
  • Create a workflow to analyze data using defined operating procedures
  • Develop forecasts using predictive analytics

You can use Qlik tools to manage data from a wide range of sources such as:

  • Azure
  • SAP
  • MongoDB
  • Salesforce
  • OpenAI
  • MySQL

If your organization has more non-technical business users than full-fledged data analysts, Qlik Sense is a good option.

Your data team can contribute insights and set up reporting workflows, and your business users can continue exploring the data on their own with the tool’s interactive visualizations and simple prompts.

How much does Qlik Sense cost?

You can use a hosted, cloud-based version of Qlik or install a local solution that you manage internally.

Access to Qlik Cloud Analytics platforms starts at $20 per user per month when billed annually. You must purchase a minimum of 10 user licenses.

If you are interested in an on-premise Qlik solution, you will need to contact the company for a demo and quote.

2. Jupyter

Jupyter is a free data science toolkit.

One of the most helpful components of the Jupyter ecosystem is Notebook, a data analysis tool. When creating Jupyter Notebooks, you can use code to combine relevant data, visuals, equations, and explanations into a single document.

The Jupyter tutorial for this process, written in a notebook, is an excellent example of how to display code, data, and graphs side by side when compiled:

Comprehensive notebooks make it simple to discuss your work and results with fellow data scientists.

Jupyter is a platform for technical users; to use its capabilities, you’ll need to be familiar with a programming language such as Python or R. If you or your team are not comfortable dealing with code, you should try another Qlik or a tool that accepts natural language input.

How much does Jupiter cost?

There are no fees to use the free, open-source Jupyter software.

3. Looker

Looker is a business intelligence and data analysis tool from Google.

Data analysts use this tool to:

  • Explore data modeling related to business metrics
  • Obtain and analyze public data sets
  • Create integrated analytical applications for use within your company
  • Visualize data through charts and graphs.

There’s also a powerful Looker Marketplace that allows you to connect pre-built code blocks, datasets, and applications, including:

  • Amazon Redshift
  • AWS Cloud Cost Management
  • Hubspot Marketing
  • Jira
  • Zendesk
  • Snowflake
  • Public COVID-19 datasets
  • Weather datasets
  • Google’s machine learning accelerator

Looker is a strong tool designed for expert analytics users. It is not user-friendly for enterprise use cases, but you can develop complete dashboards with data visualizations and share them with your non-technical colleagues.

How much does Looker cost?

Looker, unlike the free Looker Studio app, is part of Google Cloud. This service uses pay-as-you-go pricing based on the type and amount of computing you do.

If you’re interested, you’ll need to contact Google for more information on expected pricing.

4. Microsoft Power BI

Microsoft Power BI is a data analysis and integration tool.

Similar to Qlik, Microsoft Power BI includes integrated AI features that facilitate data visualization for both technical and non-technical users.

To establish a OneLake data center using Microsoft Power BI, you’ll need to link several data sources. Here, you will proceed with the processing, analysis, and visualization of data points.

You can pull data in from sources like:

  • IBM databases
  • MySQL databases
  • Oracle databases
  • Excel documents
  • SharePoint lists
  • Snowflake
  • SAP Business Warehouse

Large businesses can find the platform useful as it can accommodate thousands of users. Using more Microsoft technologies is also a smart move for your business; for example, you may incorporate Power BI data analysis findings into PowerPoint presentations.

5. Altair Rapidminer

Altair Rapidminer is an artificial intelligence and data analysis platform.

Altair Rapidminer, like Qlik and Microsoft Power BI, provides solutions for both technical and business users.

Use the tool for:

  • Import data from unstructured sources, like spreadsheets and PDF files.
  • Train machine learning processes with simple, code-free tools.
  • Develop interactive visualizations, decision trees, and simulations for your team to view.
  • Use the code to get deeper into data analysis.
  • Continuously analyze data in real time and display outcomes on dashboards.

You can utilize Altair Rapidminer in the cloud, on-premises, or a combination of the two.

How Much Does the Altair RapidMiner Cost?

Altair Rapidminer tailors plan to clients’ demands and usage, so you’ll need to contact the company for a price.

6. Databricks

Databricks is an AI-based data storage and analysis tool.

It functions as a data lake and warehouse, allowing you to store data from multiple sources without creating silos. After importing your datasets into Databricks, you will be able to:

  • Writing code in Python, R, Scala, and SQL.
  • Use low-code technologies to accelerate the coding process.
  • Generate interactive data visualizations.
  • Create data dashboards for sharing with others.

Similar to Jupyter, you may collect your data, graphs, and comments in a notebook. Databricks, unlike Jupyter, is a cloud-based tool, which certain teams may prefer.

Databricks also lets you train generative AI algorithms and machine-learning language models using your datasets.

How much do data templates cost?

Databricks offers a pay-per-use pricing approach, starting at seven cents per unit of computing power. The overall cost varies based on the features used and the units consumed.

  1. Apache Spark

Apache Spark is an open-source data analysis and engineering platform.

It is suitable for large amounts of data preparation and analysis. You can work with up to a petabyte of data without having to specify a smaller sample.

After connecting to structured or unstructured data sources, you can start by:

  • Conduct large-scale data analysis
  • Training machine learning algorithms.
  • Create reporting dashboards as you work

In addition, the platform is compatible with programming languages such as:

  • Peyton
  • SQL
  • size
  • Java
  • R

However, there is no way to use Apache Spark without knowing one of these languages, making it a suitable platform for highly technical users.

How much does Apache Spark cost?

Apache Spark is free and self-hosted software.

8. ThoughtSpot

ThoughtSpot is a self-service, AI-powered analytics tool designed for corporate teams.

The ThoughtSpot platform allows you to extract, analyze, and visualize data from a variety of sources, including:

  • Snowflake
  • inspiration
  • Amazon RDS
  • SQL in Google Cloud
  • Data tables

It’s like Altair Rapidminer and Qlik where technical analysts can use code to work with data, while business users can explore information intuitively by asking questions in natural language.

However, technical users can also benefit from ThoughtSpot’s AI: it can help write code using SQL, R, and Python.

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