What is Apache Zeppelin and what are its top alternatives?
Apache Zeppelin is a web-based notebook that allows data engineers, data analysts, and data scientists to perform interactive data analytics and visualization. It supports multiple programming languages like Scala, Python, R, and SQL, and provides built-in integrations with popular data processing frameworks like Apache Spark, Flink, and Hive. Users can write and execute code, visualize results, and collaborate with others in real-time. However, Zeppelin has some limitations like lack of advanced security features and scalability issues with large datasets.
- Jupyter Notebook: Jupyter Notebook is a popular open-source web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. It supports over 40 programming languages and provides a rich ecosystem of extensions and integrations. Pros include a large user community, extensive documentation, and support for various data science libraries. Cons include limited built-in support for big data processing and scalability issues with large datasets.
- Databricks: Databricks provides a cloud-based platform built on top of Apache Spark for data engineering, data science, and machine learning. It offers collaborative notebooks, cluster management, and integrated data processing capabilities. Pros include seamless integration with Spark, optimized performance, and automated resource management. Cons include pricing based on usage and limited support for on-premises deployments.
- Mode Analytics: Mode Analytics is a collaborative analytics platform that combines a SQL editor, Python and R notebooks, and interactive visualizations. It allows teams to explore data, create reports, and share insights with stakeholders. Pros include user-friendly interface, enterprise-grade security, and advanced analytics features. Cons include limited support for big data processing and fewer integrations compared to other tools.
- Databand: Databand is a data pipeline observability and orchestration platform that helps data teams monitor, troubleshoot, and optimize their data workflows. It provides interactive notebooks for data exploration, job scheduling capabilities, and actionable insights for improving data quality and performance. Pros include automated data lineage tracking, customizable monitoring alerts, and seamless integration with existing data tools. Cons include a learning curve for new users and limited community support.
- Dataiku: Dataiku is a collaborative data science platform that enables teams to build and deploy data pipelines, machine learning models, and visualizations. It provides a visual interface for data preparation, model building, and operationalization of AI projects. Pros include drag-and-drop interface, automated machine learning tools, and enterprise-grade security features. Cons include pricing based on usage and limited support for advanced analytics functionalities.
- RStudio: RStudio is an integrated development environment (IDE) for R programming language that includes a notebook interface for interactive data analysis and visualization. It supports R Markdown for creating reproducible reports and Shiny for building interactive web applications. Pros include extensive libraries for statistical computing, publication-ready graphics, and seamless integration with version control systems. Cons include limited support for big data processing and scalability issues with large datasets.
- Superset: Apache Superset is a modern data exploration and visualization platform that allows users to create and share interactive dashboards. It supports a wide range of data sources, custom visualizations, and collaborative features. Pros include lightweight deployment, SQL editor integration, and extensible architecture for adding custom functionality. Cons include lack of support for advanced analytics features and limited scheduling capabilities compared to other tools.
- KNIME: KNIME is an open-source data analytics platform that enables users to create workflows combining data sources, data transformation steps, and machine learning algorithms. It provides a visual programming interface, reusable components, and integration with various data processing libraries. Pros include drag-and-drop interface, comprehensive set of nodes for data wrangling, and large community of users and contributors. Cons include limited support for real-time data processing and complex data pipelines.
- H2O.ai: H2O.ai is an open-source machine learning platform that provides tools for building and deploying models at scale. It includes an interactive notebook interface, automated machine learning capabilities, and integration with popular programming languages like Python and R. Pros include fast model training, automatic feature engineering, and support for distributed computing. Cons include limited support for deep learning algorithms and custom model deployment options.
- Trino: Trino, formerly known as Presto, is a distributed SQL query engine for querying data across multiple data sources. It provides a notebook interface for interactive data exploration, support for federated queries, and high performance for analytical workloads. Pros include fast query processing, flexible data connectors, and modular architecture for enhancing functionality. Cons include complex setup and configuration process, limited support for transactional operations, and lack of built-in data visualization tools.
Top Alternatives to Apache Zeppelin
- Tableau
Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click. ...
- Kibana
Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch. ...
- RStudio
An integrated development environment for R, with a console, syntax-highlighting editor that supports direct code execution. Publish and distribute data products across your organization. One button deployment of Shiny applications, R Markdown reports, Jupyter Notebooks, and more. Collections of R functions, data, and compiled code in a well-defined format. You can expand the types of analyses you do by adding packages. ...
- Jupyter
The Jupyter Notebook is a web-based interactive computing platform. The notebook combines live code, equations, narrative text, visualizations, interactive dashboards and other media. ...
- Hue
It is open source and lets regular users import their big data, query it, search it, visualize it and build dashboards on top of it, all from their browser. ...
- IPython
It provides a rich architecture for interactive computing with a powerful interactive shell, a kernel for Jupyter. It has a support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing. ...
- Superset
Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought. ...
- Power BI
It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. ...
Apache Zeppelin alternatives & related posts
- Capable of visualising billions of rows6
- Intuitive and easy to learn1
- Responsive1
- Very expensive for small companies3
related Tableau posts
Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.
Hello everyone,
My team and I are currently in the process of selecting a Business Intelligence (BI) tool for our actively developing company, which has over 500 employees. We are considering open-source options.
We are keen to connect with a Head of Analytics or BI Analytics professional who has extensive experience working with any of these systems and is willing to share their insights. Ideally, we would like to speak with someone from companies that have transitioned from proprietary BI tools (such as PowerBI, Qlik, or Tableau) to open-source BI tools, or vice versa.
If you have any contacts or recommendations for individuals we could reach out to regarding this matter, we would greatly appreciate it. Additionally, if you are personally willing to share your experiences, please feel free to reach out to me directly. Thank you!
- Easy to setup88
- Free65
- Can search text45
- Has pie chart21
- X-axis is not restricted to timestamp13
- Easy queries and is a good way to view logs9
- Supports Plugins6
- Dev Tools4
- More "user-friendly"3
- Can build dashboards3
- Out-of-Box Dashboards/Analytics for Metrics/Heartbeat2
- Easy to drill-down2
- Up and running1
- Unintuituve7
- Works on top of elastic only4
- Elasticsearch is huge4
- Hardweight UI3
related Kibana posts
Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).
It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up
or vagrant reload
we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.
I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up
, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.
We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.
If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.
The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).
Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.
This is my stack in Application & Data
JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB
My Utilities Tools
Google Analytics Postman Elasticsearch
My Devops Tools
Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack
My Business Tools
Slack
- Visual editor for R Markdown documents3
- In-line code execution using blocks2
- Can be themed1
- In-line graphing support1
- Latex support1
- Sophitiscated statistical packages1
- Supports Rcpp, python and SQL1
related RStudio posts
- In-line code execution using blocks19
- In-line graphing support11
- Can be themed8
- Multiple kernel support7
- LaTex Support3
- Best web-browser IDE for Python3
- Export to python code3
- HTML export capability2
- Multi-user with Kubernetes1
related Jupyter posts
From my point of view, both OpenRefine and Apache Hive serve completely different purposes. OpenRefine is intended for interactive cleaning of messy data locally. You could work with their libraries to use some of OpenRefine features as part of your data pipeline (there are pointers in FAQ), but OpenRefine in general is intended for a single-user local operation.
I can't recommend a particular alternative without better understanding of your use case. But if you are looking for an interactive tool to work with big data at scale, take a look at notebook environments like Jupyter, Databricks, or Deepnote. If you are building a data processing pipeline, consider also Apache Spark.
Edit: Fixed references from Hadoop to Hive, which is actually closer to Spark.
Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead
related Hue posts
- Interactive exploration then save to a script1
- Persistent history between sessions1
- It's magical are just that1
- Help in a keystroke1
related IPython posts
Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead
- Awesome interactive filtering13
- Free9
- Wide SQL database support6
- Shareable & editable dashboards6
- Great for data collaborating on data exploration5
- User & Role Management3
- Easy to share charts & dasboards3
- Link diff db together "Data Modeling "4
- It is difficult to install on the server3
- Ugly GUI3
related Superset posts
Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.
I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.
For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.
Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.
Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.
Future improvements / technology decisions included:
Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic
As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.
One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.
Need to create a dashboard with a variety of charts having a good drill-down feature with good UI/UX and easy to manage users and roles with some authentication. I am confused between Superset and Metabase, so please suggest.
- Cross-filtering18
- Database visualisation2
- Powerful Calculation Engine2
- Access from anywhere2
- Intuitive and complete internal ETL2
- Azure Based Service1
related Power BI posts
Looking for the best analytics software for a medium-large-sized firm. We currently use a Microsoft SQL Server database that is analyzed in Tableau desktop/published to Tableau online for users to access dashboards. Is it worth the cost savings/time to switch over to using SSRS or Power BI? Does anyone have experience migrating from Tableau to SSRS /or Power BI? Our other option is to consider using Tableau on-premises instead of online. Using custom SQL with over 3 million rows really decreases performances and results in processing times that greatly exceed our typical experience. Thanks.
Which among the two, Kyvos and Azure Analysis Services, should be used to build a Semantic Layer?
I have to build a Semantic Layer for the data warehouse platform and use Power BI for visualisation and the data lies in the Azure Managed Instance. I need to analyse the two platforms and find which suits best for the same.