Alteryx is a popular data science and analytics automation software program, but Alteryx can be a bit expensive. Additionally, Alteryx may be a bit overpowered for the feature set that you need for your data science or business intelligence team. For these reasons, you may be looking for other alternatives, and want to understand the marketplace a bit better before committing to a solution.
New data science software solutions are coming to market all the time so be sure to go through our review of Alteryx alternatives before making a decision. Many of the data science platforms that we’re going to go through are going to be a great fit for teams who don’t need all of the features that Alteryx offers. We split those into three groups: one that focuses on data integration, embedded analytics, and reporting, the second major group is a little bit light on reporting and does more around integrations and customized data workflows, and finally, we offer a few open source solutions. There is some overlap among the groups as many platforms do more than one thing, but we hope this list will help you in your decision making.
If you need versatile functionality, including data integrations, the ability to create and schedule data workflows, and support slick reporting and presentation modes, Einblick is definitely worth a second look. An Alteryx alternative, without breaking the bank.
Einblick is a great alternative to Alteryx
At Einblick, our core goal is to make data science easier for data scientists by removing pain points, such as laborious environment setup, and making repetitive tasks like exploratory data analysis (EDA) easier and more intuitive. If you’re looking for a solution that allows you to use a lot of Python or SQL code, and you want your solution to be friendly to less technical team members and stakeholders, Einblick is a great choice for you. All the flexibility of Python, without sacrificing accessibility.
Currently, data science workflows tend to include Jupyter notebooks or Python notebooks, which are linear. There are some challenges when it comes to working in Python notebooks, since it is hard to collaborate and share work, and can be tedious to iterate on visualizations and models as users have to constantly scroll up and down long notebooks. To solve these pain points, Einblick includes a notebook import feature, so that data scientists can save all their existing work, while benefiting from Einblick’s unique benefits.
In-editor tutorials
No matter how familiar you are with other data science platforms, every tool has unique features with a learning curve. Einblick provides a short tutorial as part of its onboarding experience. It also provides fast response times to questions from customer support and a mature knowledge base. We also offer several tutorials and in-editor help, which you can find in multiple places. While in our canvases, you can always open or close the help documentation in our menu, which will appear in a panel on the left side. For more information about any operator, you can click on the question mark icon in the bottom left corner of the operator. Additionally, for some of our more advanced operators, we have a built-in wizard, accessible via the wand icon in the bottom left corner. Once you click the wizard, such as in our AutoML operator, the wizard will walk you through step-by-step, how to set up and use that particular operator.
Einblick connectors
Einblick supports many data connectors including many popular databases like MySQL and Postgres, but also supports cloud data warehousing platforms like Amazon’s S3 buckets, Google’s BigQuery, Databricks, and Snowflake.
Dataset creation
Einblick makes it easy to import data to create datasets for later use. This includes an advanced import feature that allows you to work with data that has non-standard formatting. You can also upload your data in the form of a CSV or TSV file.
Data preparation
The first step towards creating a data workflow is dragging out a dataset onto the canvas. In addition to representing the data, the object on the canvas comes with built-in functionality that can do data preparation, like profiling and filtering data. You can also use SQL and Python to restructure the data, scale variables, group variables, and more. Einblick is a great platform for getting your data ready before building and tuning machine learning models.
Einblick operators and AutoML
Einblick has many different families of cells that allow you a lot of flexibility and functionality, such as writing Python code, writing SQL code, cleaning data, machine learning, statistical analysis, data transformation, and visualization.
AutoML is an example of a more sophisticated feature that can be worked into a data workflow. The AutoML operator creates multiple machine learning models in seconds on up to terabytes of data using our progressive computation engine. You can configure the AutoML operator to divide your dataset into a training and testing dataset, or just build and run the model on the training set.
Presentation mode
There are a number of ways to present and share your work in Einblick. Currently, these include: dashboards, live mode, and bookmarks. In Einblick, you can create interactive dashboards. These are great if you need to share key results with stakeholders. You can create dashboards from a given canvas using the dashboard icon, or you can also create a dashboard from an operator. Then you can add any additional operators you want to the dashboard.
In our canvases, you can collaborate in real-time with other Einblick users. You can see everyone’s cursors as well as their names as you all work. If working with stakeholders not on your team, you can give them View-only permissions as well. Our lock-on mode lets you follow someone else’s cursor automatically to see what they’re doing as they work on the canvas. When in our unique live mode, you can chat and see each other on the canvas as you work and discuss your project.
Lastly, you can use Einblick bookmarks to save canvas positions for you or others to go to later. If you need to present your results, this is a great way to jump between areas of the canvas, as you would in a slide deck, while keeping everything neat and organized. The benefits of bookmarks are that you do not need to move from different applications, such as from a Python notebook to Google Slides or Powerpoint, making it easier to update visualizations and descriptions dynamically. You can also always rename your bookmark, update the position in the canvas, reorder, or delete bookmarks as needed.
Alteryx alternatives: Embedded analytics
It’s possible that when you’re searching for an Alteryx alternative you’re looking for something cheaper and potentially you don’t want to pay as much because you don’t need all the features that Alteryx provides. For example, your team may just be looking for data integration and reporting, and the ability to share those reports with your team and other stakeholders. Alteryx alternatives that focus on embedded analytics might be the way to go. Here are some popular business intelligence software programs that provide those features:
TIBCO Jaspersoft
Arguably belongs under open source analytics tools but Jaspersoft offers a number of paid tiers. Jaspersoft focuses on giving you the ability to configure reports and integrate data. It also does a decent job of solving the problem of being able to create and distribute reports to many different people and creating common dashboards that could be easily shared with a team.
TIBCO Jaspersoft belongs under the Alteryx alternatives that do embedded analytics because unlike Einblick, at the time of writing, Jaspersoft focuses much more heavily on the reporting, dashboarding side of data science, rather than on data automation, machine learning, or advanced scheduling.
Domo
The Domo Business Cloud focuses on being a unified dashboard for multiple different systems. Additional features supported by Domo all stem from this ability to embed analytics into a dashboard. Domo supports reporting and dashboards, data sharing, self-serve analytics, and of course, embedded analytics into your apps.
Microsoft Power BI
Another option for data analytics and business intelligence is Microsoft Power BI. Power BI is a cloud-based business intelligence service that offers a variety of data visualization, embedded analytics, and dashboarding tools.
Power BI can be used to connect to a variety of data sources, including Excel, CSV,GitHub, Microsoft Azure SQL Database, and more. Once data is connected, users can create visualizations using the drag-and-drop interface.
Dashboards and reports can be published to the web and shared with others. Microsoft also offers Power BI Mobile so you can monitor your dashboards and business from your mobile devices.
Qlik Sense
Qlik Sense offers lots of useful features for complex use cases and cloud-based analytics. It allows you to integrate and connect to dozens of data sources, including the most popular ones, such as AWS, Firebird, GitHub Microsoft SQL Server, MongoDB, Oracle, and Snowflake. Once the data is in Qlik Sense, you can generate visualizations, dashboards, reports, and more from that data. Qlik Sense also supports alerting based on certain triggers and interactive data dashboards and embedded analytics in external portals, business applications, public websites, and commercial software products, which is why we included in the section for Alteryx alternatives that feature embedded analytics.
Tableau
Tableau offers a number of products and solutions in the data science space. They are well-known for their data visualizations and dashboarding capabilities. Tableau has expanded over time, and offers both on-premise and cloud solutions that allow companies to combine all their data from multiple sources and work with it all in one place. There can be a steep learning curve to implement but with that comes rich features.
Tableau Public
A free Alteryx alternative that focuses on data visualization is Tableau Public. Tableau Public is a free software that can be used by anyone to create and share interactive data visualizations.
Tableau Public does not require any programming skills and can be used to connect to a variety of data sources, including Excel, CSV, and Google Sheets. Once data is connected, users can drag-and-drop to create visualizations.
Tableau Public also offers a variety of sample datasets and templates to help users get started. Visualizations can be published to the web and embedded on websites and blogs.
In Tableau, a sequence of visualizations is called a story, and each individual sheet in a workbook is called a story point. You can think of this as users need to create a narrative around their data visualizations. This can be especially helpful when trying to communicate complex data sets to a non-technical audience.
Alteryx alternatives: Data workflows
Some of the data science software platforms are more about creating data workflows and rely on integrations with third-party systems to really handle any kind of sophisticated reporting or embedded analytics. Depending on what you are looking for that may be all you need.
Talend
Talend is a low-code platform that unifies data. One thing that stands out about this option is that even though Talend’s products are paid, they offer free trials and there is an open source product called Talend Open Studio for building basic data pipelines. Talend has many features available, but may not be your tool of choice if you want to drill down into the technical weeds to create your own custom code. Talend is an ETL tool that supports data quality, data integration, and data governance. There are two main products: Talend Data Fabric and Talend Stitch. If you’re looking for a solution that offers both low-code optionality AND the ability to write custom Python or SQL code, Einblick may be a better choice.
Looker
Looker is another option that focuses on data management and automated workflows. Looker’s platform has split the features into four main areas: analytics and dashboarding, integrated insights, workflows, and custom applications. Its data-driven workflows allow you to update data regularly and other features that you would expect from a data science software. Looker provides enhancements to the platform via their Marketplace, where you can browse pre-built applications, plug-ins, and more. Looker provides pre-built sections of code called “blocks” that you can use to leverage the platform’s capabilities and product partnerships. These blocks are also available in the Looker Marketplace. Blocks can be anything from SQL patterns to custom visualizations or machine learning models.
Pyramid Analytics
Pyramid Analytics is a business intelligence platform that offers a unified platform for data preparation, business analytics, and data science. They refer to themselves as a digital decision intelligence platform. They separate their key capabilities into three categories: data prep, business analytics, and data science. We’ve included Pyramid Analytics in the embedded analytics section because they only offer custom workflows in higher paid tiers.
Open source Alteryx alternatives
Dataverse
The Dataverse Project (Dataverse), not to be confused with Microsoft Dataverse, began at Harvard University’s Institute for Quantitative Social Science. Dataverse is part of a suite of software projects including the Harvard Dataverse Network. Dataverse is a web application for sharing, preserving, citing, analyzing, and discovering research data. It enables data producers to deposit data and code in a central repository and provides a persistent identifier (DOI) for each data set. Data consumers can search for and access customizable data sets using the DOI. You can create your own dataverse, which serves as a container for datasets, files, and metadata. The software also includes features for data curation, such as versioning, data provenance tracking, and dataset linking.
The Dataverse software is open source and available for anyone to download and install. The Harvard Dataverse Network is a managed service that provides a central instance of the Dataverse software, along with support and user documentation.
Redash
Redash is an open source platform, available for anyone to download and deploy. New SQL users can leverage Redash to explore, query, visualize, and share results from many data sources in a low-code environment. Data consumers can access datasets using the Redash Query Editor, which offers a point-and-click interface for running SQL queries. The Query Editor also includes user-friendly low-code features, such as auto-completion and syntax highlighting.
Redash includes a drag-and-drop interface for creating data visualizations. Visualizations can be shared with others by embedding them or downloading the visualizations as image files.
Programming languages (Python, R)
Two open source Alteryx alternatives that are going to show up every time are programming languages, R and Python. Programming languages require more technical resources and human time than many of these other solutions, but they could be the right fit for your team.
R is a free and open-source programming language for statistical computing and graphics. R is widely used by statisticians and data scientists for data analysis and visualization. R offers a wide variety of statistical and graphical techniques and is extendable through packages, such as ggplot2, dplyr, and tidyr.
There are many IDEs (integrated development environments) available for R, such as RStudio, which make R programming easier. RStudio is a free and open-source IDE for R. RStudio includes a code editor, debugging tools, and support for working with R Markdown files. R Markdown is a file format for creating reproducible reports, presentations, dashboards, and reports in R.
Python is a widely used, open source, general-purpose programming language with many modules, libraries, and packages for data analysis, visualization, data science, and machine learning. Python is easy to learn for beginners and has a large and active community.
Due to its versatility and functionality, Python has become a favorite language among data scientists and the data community in general. There are tons of popular packages that help to augment and operationalize data science and data visualization practices. For example, scikit-learn is a popular package for data science and machine learning. Using scikit-learn you can easily split your data into training and testing datasets; you can run linear regression and logistic regression models, as well as k-means clustering and other supervised and unsupervised machine learning algorithms. Other popular Python packages include statsmodels, pandas, and numpy for statistics and working with data. Python also has great visualization packages to choose from, such as matplotlib, seaborn, and plotly.
The Konstanz Information Miner (KNIME)
KNIME offers two main products: KNIME Analytics Platform and KNIME Server. KNIME Analytics Platform is an open-source software alternative to Alteryx. KNIME also offers a commercial product called KNIME Server. KNIME Analytics Platform is readily compatible with a variety of data sources, including relational databases and NoSQL databases.
KNIME Analytics Platform has an easy-to-use interface for exploring data, preparing it for modeling, training models, and testing them. Its capabilities extend beyond data visualization to predictive modeling and machine learning, with pre-built nodes for a variety of algorithms.
KNIME’s advantage over similar tools such as Tableau and Qlik Sense is its ability to easily incorporate R and Python scripts in workflows.
Frequently asked questions
Alteryx features include data input and output, data blending, data manipulation, predictive analytics, and spatial analysis.
Einblick is a data preparation tool that is an alternative to Alteryx. Einblick offers a code-free interface for data preparation, allowing users to visually explore, transform, and cleanse data.
The main difference between Alteryx and Einblick is that Alteryx is a visual workflow tool that combines Extract, Transform and Load (ETL) capabilities with spatial processing while Einblick is a data preparation tool that offers a code-free interface for data preparation, allowing users to visually explore, transform, and cleanse data.
It depends on your needs. If you need a visual workflow tool that combines Extract, Transform and Load (ETL) capabilities with spatial processing, then Alteryx may be a good choice. If you need a data preparation tool that offers python and SQL code operators as well as no-code operators for data preparation and exploration, then Einblick is the better choice.
The main difference between Alteryx and Trifacta is that Alteryx is a visual workflow tool that combines Extract, Transform and Load (ETL) capabilities with spatial processing while Trifacta is a data wrangling tool that offers a point-and-click interface for data transformation, allowing users to quickly and easily clean, shape, and enrich data.
It depends on your needs. If you need a visual workflow tool that combines Extract, Transform and Load (ETL) capabilities with spatial processing, then Alteryx is the better choice. If you need a data wrangling tool that offers a point-and-click interface for data transformation, then Trifacta is the better choice.
The main difference between Alteryx and Knime is that Alteryx is a visual workflow tool that combines Extract, Transform and Load (ETL) capabilities with spatial processing while Knime is an open source data analytics platform that provides a variety of features for data preparation, mining, modeling, and visualization.
It depends on your needs. If you need a visual workflow tool that combines Extract, Transform and Load (ETL) capabilities with spatial processing, then Alteryx is the better choice. If you need an open source data analytics platform that provides a variety of features for data preparation, mining, modeling, and visualization, then Knime is the better choice.
The main difference between Alteryx and Talend is that Alteryx is a visual workflow tool that combines Extract, Transform and Load (ETL) capabilities with spatial processing while Talend is an open source data integration tool that provides a graphical interface for configuring ETL jobs.
It depends on your needs. If you need a visual workflow tool that combines Extract, Transform and Load (ETL) capabilities with spatial processing, then Alteryx is the better choice. If you need an open source data integration tool that provides a graphical interface for configuring ETL jobs, then Talend is the better choice.
About
Einblick is an agile data science platform that provides data scientists with a collaborative workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick customers include Cisco, DARPA, Fuji, NetApp and USDA. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.