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  1. Stackups
  2. Stackups
  3. Iteratively vs Rakam

Iteratively vs Rakam

OverviewComparisonAlternatives

Overview

Rakam
Rakam
Stacks1
Followers4
Votes0
Iteratively
Iteratively
Stacks6
Followers16
Votes0

Iteratively vs Rakam: What are the differences?

Iteratively: The best way to track your product analytics. Iteratively helps teams capture reliable product analytics they can trust. It eliminates the most common causes of error during the definition and implementation of tracking plans, and cuts down on the time it takes to correctly instrument the product. As a result, folks that consume product analytics get exactly what they spec'd out and can rely on the incoming data knowing it is trustworthy and accurate; Rakam: Custom Analytics Platform. You can build custom reports or custom dashboards just connect Rakam with third-party tools or join Rakam data with internal data sources. A full stack analytics platform for you, including both backend and frontend.

Iteratively and Rakam can be categorized as "Custom Analytics" tools.

Detailed Comparison

Rakam
Rakam
Iteratively
Iteratively

You can build custom reports or custom dashboards just connect Rakam with third-party tools or join Rakam data with internal data sources. A full stack analytics platform for you, including both backend and frontend.

Iteratively helps teams capture reliable product analytics they can trust. It eliminates the most common causes of error during the definition and implementation of tracking plans, and cuts down on the time it takes to correctly instrument the product. As a result, folks that consume product analytics get exactly what they spec'd out and can rely on the incoming data knowing it is trustworthy and accurate.

BI Reporting, Funnel Analysis, Retention Analysis, Event Explorer
Single source of truth for your product analytics events; Generates a strongly typed library for your analytics with cross-platform support (Typescript, Kotlin, Swift, etc..); Integrates with the 3rd party and custom backends (Amplitude, Mixpanel, Segment, Intercom, etc...); Established best practices for taxonomy and naming convention; Quality assurance built in with code linting and runtime analysis; Generate automatic & beautiful documentation
Statistics
Stacks
1
Stacks
6
Followers
4
Followers
16
Votes
0
Votes
0
Integrations
Intercom
Intercom
Stripe
Stripe
Google Analytics
Google Analytics
Mailgun
Mailgun
Mixpanel
Mixpanel
Pipedrive
Pipedrive
GitHub
GitHub
Google Analytics
Google Analytics
Slack
Slack
Intercom
Intercom
Segment
Segment
Mixpanel
Mixpanel
Amplitude
Amplitude
Jira
Jira
FullStory
FullStory
Snowplow
Snowplow

What are some alternatives to Rakam, Iteratively?

Segment

Segment

Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch.

Keen

Keen

Keen is a powerful set of API's that allow you to stream, store, query, and visualize event-based data. Customer-facing metrics bring SaaS products to the next level with acquiring, engaging, and retaining customers.

Snowplow

Snowplow

Snowplow is a real-time event data pipeline that lets you track, contextualize, validate and model your customers’ behaviour across your entire digital estate.

Quickmetrics

Quickmetrics

It is a service for collecting, analyzing and visualizing custom metrics. It can be used to track anything from signups to server response times. Sending events is super simple.

Ahoy

Ahoy

Ahoy provides a solid foundation to track visits and events in Ruby, JavaScript, and native apps.

Bitdeli

Bitdeli

Build dashboards and reports with exactly the metrics you need using plain Python scripts. There is nothing new to learn. Bitdeli keeps your results up to date, no matter how much data you have or how complex your metrics are. Get started in minutes with our growing library of open-source analytics, created by experienced data hackers.

Google Tag Manager

Google Tag Manager

Tag 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.

Rudderstack

Rudderstack

RudderStack allows you to easily build pipelines connecting your whole customer data stack, then make them smarter by pulling analysis from your data warehouse to trigger enrichment and activation in customer tools.

Dagster

Dagster

It is an orchestrator that's designed for developing and maintaining data assets, such as tables, data sets, machine learning models, and reports.

Astronomer

Astronomer

Astro is the modern data orchestration platform, powered by Apache Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code.

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