Azure Analysis Services vs Datameer
Overview
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CLI (Node.js)
Manual
Detailed Comparison
It is a single application that helps you get any data into Hadoop, bring it together, analyze it, and visualize it as quickly and easily as possible. No coding required. Everything in it is self-service and intuitive, from our wizard-based data integration, to a spreadsheet with point-and-click analytics, to our blank canvas to for building custom visualizations. | It is a fully managed platform as a service (PaaS) that provides enterprise-grade data models in the cloud. Use advanced mashup and modeling features to combine data from multiple data sources, define metrics, and secure your data in a single, trusted tabular semantic data model. The data model provides an easier and faster way for users to perform ad hoc data analysis using tools like Power BI and Excel. |
Data integration;
Data visualization;
Dynamic data management;
Open infrastructure;
Pre-built application;
Self-service analytics. | Get started quickly and scale with efficiency; Transform complex data into one version of the truth; Match performance to the speed of business; Provide secured access anytime, from anywhere |
Statistics | |
Stacks 5 | Stacks 11 |
Followers 12 | Followers 15 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to Datameer, Azure Analysis Services?
Metabase
It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.
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.
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.
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.
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.
Cube
Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.
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.
