Alation vs Iteratively
Overview
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CLI (Node.js)
Manual
Detailed Comparison
The leader in collaborative data cataloging, it empowers analysts & information stewards to search, query & collaborate for fast and accurate insights. | 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. |
Data Catalog; Automatically indexes your data by source; Automatically gathers knowledge about your data | 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 14 | Stacks 6 |
Followers 26 | Followers 16 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to Alation, Iteratively?
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 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.
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Presto
Distributed SQL Query Engine for Big Data
Amazon Athena
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
lakeFS
It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.
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.
Druid
Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
Apache Kylin
Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.
