Dremio vs Supermetrics.com
Overview
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Detailed Comparison
Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts. | It collects all your data in one secure place and makes it analysis-ready for all your reporting needs. Send your marketing data to a dashboard (Data Studio), spreadsheet (Sheets, Excel), or data warehouse (Google, Microsoft, Amazon, Snowflake, etc.) |
Democratize all your data;
Make your data engineers more productive;
Accelerate your favorite tools;
Self service, for everybody
| Quickly bring any metrics and dimensions from your favorite marketing platforms into your go-to reporting, data visualization, data warehousing, or BI tool. No sampling. No nonsense. Just clean data;
Once you have your data where you want it, you can start organizing and filtering it immediately. Dive into your numbers to figure out what is and isn’t working — and then get straight into optimization;
When you’ve built your report or dashboard, you can eliminate hours of manual work by scheduling data transfers and automating your marketing reporting |
Statistics | |
Stacks 116 | Stacks 3 |
Followers 348 | Followers 3 |
Votes 8 | Votes 0 |
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What are some alternatives to Dremio, Supermetrics.com?
Google BigQuery
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
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.
Amazon Redshift
It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
Qubole
Qubole is a cloud based service that makes big data easy for analysts and data engineers.
Presto
Distributed SQL Query Engine for Big Data
Amazon EMR
It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.
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.
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.
