What is Iteratively and what are its top alternatives?
Iteratively is a data workflow monitoring tool that helps teams keep track of changes in their data pipelines and ensures data quality. It allows users to visualize and document data changes, collaborate with team members, and automate data validation processes. However, some limitations of Iteratively include limited integrations with other data tools and a steeper learning curve for new users.
- Datafold: Datafold is a data observability platform that helps data teams ensure data quality and reliability. Key features include data monitoring, anomaly detection, and impact analysis. Pros of Datafold include advanced anomaly detection capabilities and easy integration with popular data sources, while a potential con could be a higher price point compared to Iteratively.
- Great Expectations: Great Expectations is an open-source data validation framework that helps data engineers maintain data quality. Its key features include data profiling, validation rules, and automated testing. Pros of Great Expectations are its flexibility and customization options, while a con could be the need for more technical expertise to set up and use the tool effectively.
- Fishtown Analytics dbt: dbt is a popular tool for building and managing data transformation pipelines. It allows users to version control SQL pipelines, run tests on data transformations, and document data lineage. Pros of dbt include its strong community support and active development, while a potential con could be the reliance on SQL for data transformations.
- Airflow: Apache Airflow is an open-source workflow management platform used for orchestrating complex data pipelines. Its key features include scheduling, monitoring, and task dependencies. Pros of Airflow include its scalability and customization options, while a con could be its steep learning curve for beginners.
- Prefect: Prefect is a workflow orchestration tool that helps users create, schedule, and monitor data pipelines. It offers features like DAG visualization, parameterization, and error handling. Pros of Prefect include its user-friendly interface and active community, while a con could be its comparatively smaller user base.
- Stitch: Stitch is a cloud data integration service that helps users consolidate data from multiple sources into a data warehouse. Key features include automated data loading, schema mapping, and data pipeline monitoring. Pros of Stitch include its simplicity and ease of setup, while a con could be limitations in customization and transformations.
- Matillion: Matillion is an ETL tool that enables users to design and orchestrate data transformation workflows. It offers features like drag-and-drop interface, data connectors, and scheduling capabilities. Pros of Matillion include its integration with cloud data warehouses and scalability, while a con could be the cost associated with its usage.
- Prefect Cloud: Prefect Cloud is a managed platform that provides additional features for monitoring, scaling, and orchestrating workflows created with Prefect. Pros include the added security and performance benefits of a managed platform, while a con could be the additional cost compared to the open-source version of Prefect.
- Dataform: Dataform is a data orchestration tool that helps data teams manage SQL-based transformation pipelines. Key features include version control, scheduling, and dependency management. Pros of Dataform include its focus on SQL transformations and ease of use for SQL-savvy users, while a potential con could be limitations in handling non-SQL data transformations.
- Dagster: Dagster is a data orchestrator that helps users define, schedule, and monitor data pipelines while emphasizing data quality and consistency. It offers features like solid data testing, pipeline visualization, and dependency management. Pros of Dagster include its focus on data quality and monitoring, while a con could be its learning curve for users new to the tool.
Top Alternatives to Iteratively
IterableIterable empowers growth marketers to create world-class user engagement campaigns throughout the full lifecycle, and across all channels. Marketers segment users, build workflows, automate touchpoints at scale without engineering support. ...
IterateIt is a modern survey tool built to help technology companies validate ideas, question assumptions, and understand the motivation behind their metrics. ...
NumPyBesides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...
Google AnalyticsGoogle Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications. ...
Google Tag ManagerTag Manager gives you the ability to add and update your own tags for conversion tracking, site analytics, remarketing, and more. There are nearly endless ways to track user behavior across your sites and apps, and the intuitive design lets you change tags whenever you want. ...
MixpanelMixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience. ...
- Mixpanel
Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience. ...
OptimizelyOptimizely is the market leader in digital experience optimization, helping digital leaders and Fortune 100 companies alike optimize their digital products, commerce, and campaigns with a fully featured experimentation platform. ...
Iteratively alternatives & related posts
- Segment integration6
- Powerful4
- Send the right message, at right time, to right device3
- Easy to Optimize2
related Iterable posts
Hi there, we are a seed-stage startup in the personal development space. I am looking at building the marketing stack tool to have an accurate view of the user experience from acquisition through to adoption and retention for our upcoming React Native Mobile app. We qualify for the startup program of Segment and Mixpanel, which seems like a good option to get rolling and scale for free to learn how our current 60K free members will interact in the new subscription-based platform. I was considering AppsFlyer for attribution, and I am now looking at an affordable yet scalable Mobile Marketing tool vs. building in-house. Braze looks great, so does Leanplum, but the price points are 30K to start, which we can't do. I looked at OneSignal, but it doesn't have user flow visualization. I am now looking into Urban Airship and Iterable. Any advice would be much appreciated!
related Iterate posts
- Great for data analysis10
- Faster than list4
related NumPy posts
Server side
We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.
Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.
Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.
Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.
Client side
UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.
State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.
Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.
Cache
- Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.
Database
- Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.
Infrastructure
- Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.
Other Tools
Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.
Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.
Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?
- Free1.5K
- Easy setup927
- Data visualization891
- Real-time stats698
- Comprehensive feature set406
- Goals tracking182
- Powerful funnel conversion reporting155
- Customizable reports139
- Custom events try83
- Elastic api53
- Updated regulary15
- Interactive Documentation8
- Google play4
- Walkman music video playlist3
- Industry Standard3
- Advanced ecommerce3
- Irina2
- Easy to integrate2
- Financial Management Challenges -2015h2
- Medium / Channel data split2
- Lifesaver2
- Confusing UX/UI11
- Super complex8
- Very hard to build out funnels6
- Poor web performance metrics4
- Very easy to confuse the user of the analytics3
- Time spent on page isn't accurate out of the box2
related Google Analytics posts
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
Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).
Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.
Google Tag Manager
related Google Tag Manager posts
Hi,
This is a question for best practice regarding Segment and Google Tag Manager. I would love to use Segment and GTM together when we need to implement a lot of additional tools, such as Amplitude, Appsfyler, or any other engagement tool since we can send event data without additional SDK implementation, etc.
So, my question is, if you use Segment and Google Tag Manager, how did you define what you will push through Segment and what will you push through Google Tag Manager? For example, when implementing a Facebook Pixel or any other 3rd party marketing tag?
From my point of view, implementing marketing pixels should stay in GTM because of the tag/trigger control.
If you are using Segment and GTM together, I would love to learn more about your best practice.
Thanks!
Mixpanel
- Great visualization ui144
- Easy integration108
- Great funnel funcionality78
- Free58
- A wide range of tools22
- Powerful Graph Search15
- Responsive Customer Support11
- Nice reporting2
- Messaging (notification, email) features are weak2
- Paid plans can get expensive2
- Limited dashboard capabilities1
related Mixpanel posts
Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).
Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.
Hi there, we are a seed-stage startup in the personal development space. I am looking at building the marketing stack tool to have an accurate view of the user experience from acquisition through to adoption and retention for our upcoming React Native Mobile app. We qualify for the startup program of Segment and Mixpanel, which seems like a good option to get rolling and scale for free to learn how our current 60K free members will interact in the new subscription-based platform. I was considering AppsFlyer for attribution, and I am now looking at an affordable yet scalable Mobile Marketing tool vs. building in-house. Braze looks great, so does Leanplum, but the price points are 30K to start, which we can't do. I looked at OneSignal, but it doesn't have user flow visualization. I am now looking into Urban Airship and Iterable. Any advice would be much appreciated!
Mixpanel
- Great visualization ui144
- Easy integration108
- Great funnel funcionality78
- Free58
- A wide range of tools22
- Powerful Graph Search15
- Responsive Customer Support11
- Nice reporting2
- Messaging (notification, email) features are weak2
- Paid plans can get expensive2
- Limited dashboard capabilities1
related Mixpanel posts
Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).
Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.
Hi there, we are a seed-stage startup in the personal development space. I am looking at building the marketing stack tool to have an accurate view of the user experience from acquisition through to adoption and retention for our upcoming React Native Mobile app. We qualify for the startup program of Segment and Mixpanel, which seems like a good option to get rolling and scale for free to learn how our current 60K free members will interact in the new subscription-based platform. I was considering AppsFlyer for attribution, and I am now looking at an affordable yet scalable Mobile Marketing tool vs. building in-house. Braze looks great, so does Leanplum, but the price points are 30K to start, which we can't do. I looked at OneSignal, but it doesn't have user flow visualization. I am now looking into Urban Airship and Iterable. Any advice would be much appreciated!
Optimizely
- Easy to setup, edit variants, & see results50
- Light weight20
- Best a/b testing solution16
- Integration with google analytics14
related Optimizely posts
Hey all, I'm managing the implementation of a customer data platform and headless CMS for a digital consumer content publisher. We're weighing up the pros and cons of implementing an OTB activation platform like Optimizely Recommendations or Dynamic Yield vs developing a bespoke solution for personalising content recommendations. Use Case is CDP will house customers and personas, and headless CMS will contain the individual content assets. The intermediary solution will activate data between the two for personalisation of news content feeds. I saw GCP has some potentially applicable personalisation solutions such as recommendations AI, which seem to be targeted at retail, but would probably be relevant to this use case for all intents and purposes. The CDP is Segment and the CMS is Contentstack. Has anyone implemented an activation platform or personalisation solution under similar circumstances? Any advice or direction would be appreciated! Thank you