Stitch vs Dremio

Overview

Stitch

Stacks150

Followers150

Votes12

Dremio

Stacks116

Followers348

Votes8

Stitch vs Dremio: What are the differences?

What is Stitch? All your data. In your data warehouse. In minutes. Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

What is Dremio? Self-service data for everyone. It is a data-as-a-service platform that empowers users to discover, curate, accelerate, and share any data at any time, regardless of location, volume, or structure. Modern data is managed by a wide range of technologies, including relational databases, NoSQL datastores, file systems, Hadoop, and others.

Stitch and Dremio can be primarily classified as "Big Data as a Service" tools.

Some of the features offered by Stitch are:

Connect to your ecosystem of data sources - UI allows you to configure your data pipeline in a way that balances data freshness with cost and production database load
Replication frequency - Choose full or incremental loads, and determine how often you want them to run - from every minute, to once every 24 hours
Data selection - Configure exactly what data gets replicated by selecting the tables, fields, collections, and endpoints you want in your warehouse

On the other hand, Dremio provides the following key features:

Democratize all your data
Make your data engineers more productive
Accelerate your favorite tools

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Advice on Stitch, Dremio

karunakaran

Consultant

Jun 26, 2020

Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

Create pipelines to ingest the data from multiple sources into the data lake
Help me in aggregating and filtering data available in the data lake.
Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

80.4k views80.4k

Comments

datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

We need to perform ETL from several databases into a data warehouse or data lake. We want to

keep raw and transformed data available to users to draft their own queries efficiently
give users the ability to give custom permissions and SSO
move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

319k views319k

Comments

Detailed Comparison

Stitch	Dremio
Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.	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.
Connect to your ecosystem of data sources - UI allows you to configure your data pipeline in a way that balances data freshness with cost and production database load;Replication frequency - Choose full or incremental loads, and determine how often you want them to run - from every minute, to once every 24 hours; Data selection - Configure exactly what data gets replicated by selecting the tables, fields, collections, and endpoints you want in your warehouse;API - With the Stitch API, you're free to replicate data from any source. Its REST API supports JSON or Transit, and recognizes your schema based on the data you send.;Usage dashboard - Access our simple UI to check usage data like the number of rows synced by data source, and how you're pacing toward your monthly row limit;Email alerts - Receive immediate notifications when Stitch encounters issues like expired credentials, integration updates, or warehouse errors preventing loads;Warehouse views - By using the freshness data provided by Stitch, you can build a simple audit table to track replication frequency;Scalable - Highly Scalable Stitch handles all data volumes with no data caps, allowing you to grow without the possibility of an ETL failure;Transform nested JSON - Stitch provides automatic detection and normalization of nested document structures into relational schemas;Complete historical data - On your first sync, Stitch replicates all available historical data from your database and SaaS tools. No database dump necessary.	Democratize all your data; Make your data engineers more productive; Accelerate your favorite tools; Self service, for everybody
Statistics
Stacks 150	Stacks 116
Followers 150	Followers 348
Votes 12	Votes 8
Pros & Cons
Pros 8 3 minutes to set up 4 Super simple, great support	Pros 3 Nice GUI to enable more people to work with Data 2 Connect NoSQL databases with RDBMS 2 Easier to Deploy 1 Free Cons 1 Works only on Iceberg structured data
Integrations
Stripe Twilio SendGrid Zendesk MongoDB Marketo Recurly GitLab Zapier FreshDesk Harvest	Amazon S3 Python Tableau Azure Database for PostgreSQL Qlik Sense PowerBI

What are some alternatives to Stitch, Dremio?

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.