Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.
Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects. | 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. |
Write, save, and share SQL queries with other analysts in your company; Empower non-technical folks to update queries on their own; Run queries on a schedule, create lists of related reports, and explore a project's history as it changes over time;
Build reports using standard charting or create completely customer, interactive visuals with HTML, CSS, and Javascript;Database connectors for MySQL, Postgres, Redshift, Vertica, Hive, Heroku, Segment, BigQuery, Impala;
Mode also offers SQL School (sqlschool.modeanalytics.com), a free, interactive SQL tutorial and the Mode Playbook. | Data integration;
Data visualization;
Dynamic data management;
Open infrastructure;
Pre-built application;
Self-service analytics. |
Statistics | |
Stacks 125 | Stacks 5 |
Followers 227 | Followers 12 |
Votes 17 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
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.
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.
Distributed SQL Query Engine for Big Data
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'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 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.
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 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: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.
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.
