Resources
Benchmark research, reports, and more.
Skip Navigation
FAQ
Stitch FAQ
Quick answers to questions about Stitch and Singer.
Read now →
Benchmark
The State of Data Engineering
Explore salaries, job growth, and skill sets of data engineers.
Read now →
Guide
Getting Started with Stitch Enterprise
How Stitch Enterprise meets the data needs of your organization.
Read now →
Benchmark
The State of Data Science
Just how many data scientists are there? How is the field changing?
Read now →
Guide
Setting the data strategy for your growing organization
Everything you need to know about building a data-driven company.
Read now →
Feature
Data science vs. data analytics: What they are and how to use them
Data science encompasses methods for manipulating raw data to obtain meaningful insights, while data analytics answers specific business questions.
Read now →
Feature
Data warehouse vs. data mart: a comparison
Cloud data warehouses are created quickly, and once a centralized data warehouse is operational, data marts can be spun off for business units.
Read now →
Feature
Redshift vs. Azure Synapse Analytics: comparing cloud data warehouses
Redshift and Azure Synapse Analytics both support data analytics, but differ in aspects of architecture, pricing, performance, administration, security, and compliance.
Read now →
Feature
What is an operational data store?
An operational data store (ODS) is a central database used primarily for transactional functions and operational reporting.
Read now →
Feature
What is data mining?
Data mining refers to the process of identifying within a data set patterns, trends, or anomalies. Click to learn how data mining works.
Read now →
Feature
OLTP vs OLAP: Understanding the differences and use cases
Online transaction processing (OLTP) captures, stores, and processes data from transactions. Online analytical processing (OLAP) analyzes data for insights.
Read now →
Feature
What is data extraction: tools and methods
Data extraction is the process of obtaining data from multiple sources, and moving it to a new destination designed to support online analytical processing.
Read now →
Feature
How to choose the right business intelligence tool
Stitch surveyed its customers to learn which business intelligence tools they use. These seven tools were mentioned most often.
Read now →
Feature
Tutorial: Using Google Data Studio with BigQuery and Stitch
How do you begin combining data from cloud applications with your internal databases to gain insight into your business?
Read now →
Feature
6 best practices for unlocking the value of a data warehouse
Discover best practices your organization should implement to get the most out of your data warehouse and facilitate data analytics.
Read now →
Feature
What is enterprise data management?
Enterprise data management refers to a set of processes and activities focused on data accuracy, quality, security, availability, and good governance.
Read now →
Feature
7 reasons to use Microsoft Power BI
Power BI has all the advantages of a modern BI platform, and unique benefits that distinguish it from the array of competing tools on the market today.
Read now →
Feature
6 Redshift features that change the data warehouse game
Here's a look at six features that set Redshift apart from other cloud data warehouses.
Read now →
Feature
Oracle Database: demystifying your data strategy
Oracle's ecosystem is expansive, but with the right tool, you can quickly and reliably bring your data to any cloud data warehouse, extracting the maximum value from your data with analytics and business intelligence tools.
Read now →
Feature
Best practices for data modeling
This article covers some guidelines on how to build better data models that are more maintainable, more useful, and more performant.
Read now →
Feature
Using business intelligence tools for marketing
Business intelligence (BI) tools allow enterprises to obtain valuable insights from information across all digital marketing channels.
Read now →
Feature
Marketing analytics: definition and uses
Marketing analytics is a set of technologies and methods for transforming data into marketing insights to maximize ROI from marketing initiatives.
Read now →
Feature
Tutorial: Using Redshift and Amazon QuickSight to deliver business analytics
How do you begin combining data from SaaS applications with your internal databases to gain insight into your business? In this tutorial, we’ll show you how QuickSight can help you deliver business insights.
Read now →
Feature
Unlocking big data with retail data analytics
Retail customers expect an engaging personal experience when shopping online or in a store. Retail data analytics helps organizations retain customers, and can enhance their lifetime value (LTV) to the business.
Read now →
Feature
Improving health care with business intelligence
Business intelligence (BI) leads to better health care. Learn how your organization can improve treatment outcomes and increase patient satisfaction with BI.
Read now →
Feature
Using business intelligence with big data
Enterprises can use the reporting and visualization capabilities of business intelligence (BI) tools to obtain insights from big data.
Read now →
Feature
MySQL vs. MariaDB: drop-in or diverging?
MariaDB started out as a fork of MySQL. A decade after its debut, how do the two databases differ?
Read now →
Feature
Application integration vs. data integration: how they differ
Application integration and data integration are two approaches organizations can take to make use of data from different systems, but they meet different needs.
Read now →
Feature
Best practices for data warehouse maintenance
The foundation of any organization's data analytics stack is its data warehouse. Have you given any thought to data warehouse maintenance?
Read now →
Feature
How Redshift differs from PostgreSQL
If you have SQL skills you developed from working with PostgreSQL, you'll be able to get by in Amazon Redshift pretty well – but you'll have to familiarize yourself with the differences between the two platforms.
Read now →
Feature
What is a Data Pipeline? Process and Examples
A data pipeline is a set of actions that ingests raw data from disparate sources and moves the data to a destination for storage, analysis, or business intelligence.
Read now →
Feature
4 benefits of self-service data ingestion
Learn how self-service data ingestion with an ELT tool makes it easy to replicate data and get business insights quickly.
Read now →
Feature
The causes and costs of data silos
A data silo (or information silo) is a repository of information in a department or an application that is not easily or fully accessible by other departments or applications.
Read now →
Feature
PostgreSQL vs. MySQL: 9 key criteria to drive your database decision
PostgreSQL and MySQL are two of the better known open source databases in use today, but which one is right for your organization, and why?
Read now →
Feature
Azure SQL Database: Grow your potential in the cloud
Azure SQL Database is Microsoft’s cloud-based SaaS relational database service, which is managed for availability, durability, and scalability.
Read now →
Feature
How to replicate Google Sheets to your data warehouse
Stitch now offers a Google Sheets integration! In the Stitch dashboard, choose Google Sheets and ask to be added to the public beta.
Read now →
Guide
Data Driven Advertising with Performance Marketing
Advertisers track digital advertising performance to properly drive value. Digital marketing strategy is nothing without data, make sure you know what you need to capture.
Read now →
FAQ
What is Google Analytics 4?
Today, your web analytics tool needs to include mobile app data to successfully track customer events. Watch this video for a step-by-step tutorial of connecting your GA4 data with the rest of your data pipeline.
Read now →
Feature
Data ingestion: the first step to a sound data strategy
Data ingestion is the transportation of data from assorted sources to a storage medium where it can be accessed, used, and analyzed by an organization.
Read now →
Guide
How Google BigQuery Compares as a Data Warehouse
Compare a free-to-start cloud warehouse solution like Google BigQuery to Amazon Redshift, Snowflake, and Microsoft Azure, and see how to quickly set up a data warehouse in minutes.
Read now →
Feature
Amazon Redshift vs. Google BigQuery: a comparison
Redshift and BigQuery have many similarities, but also important differences that can tip the scales in a cloud data warehouse comparison.
Read now →
Feature
Understanding data replication and its impact on business strategy
One common use of data replication is for disaster recovery, to ensure that an accurate backup exists at all times in case of a catastrophe, hardware failure, or a system breach where data is compromised.
Read now →
Feature
Snowflake vs. Redshift: choosing a modern data warehouse
Successful businesses depend on sound intelligence, and as their decisions become more data-driven than ever, it's critical that all the data they gather reaches its optimal destination for analytics: a high-performing data warehouse in the cloud.
Read now →
Feature
Data visualization and your business
Data visualization encompasses any method for displaying data visually to reveal useful trends and insights. It's a key aspect of business intelligence.
Read now →
Feature
Top ETL options for AWS data pipelines
Finding the best AWS ETL process for your business can make the difference between working on your data pipeline or making your data pipeline work for you.
Read now →
Feature
5 steps for choosing a cloud data warehouse
Learn about the most popular cloud data warehouses' key features and the criteria to use when evaluating them.
Read now →
Feature
How to transfer your data to Amazon S3
When moving data to S3, you can choose among the many services offered by AWS and third parties for everything from large migrations to streaming data.
Read now →
Feature
Improve your data team's productivity through automated data analytics
Automated data analytics is the practice of using computer systems and processes to perform analytical tasks with little or no human intervention.
Read now →
Feature
Resolving four common Snowflake data ingestion barriers with Stitch
Learn about the best Snowflake data ingestion methods using various different formats and volume of data.
Read now →
Feature
What is AWS S3?
Amazon S3, or simple storage service, is a cloud storage solution provided by Amazon Web Services. Use cases for AWS S3 include data lakes for big data analytics, and data archiving.
Read now →
Feature
What is Data Migration?
Agile, scalable cloud-based data migration tools handle rapidly changing business needs with pay-as-you-go pricing.
Read now →
Feature
BigQuery vs. Azure Synapse Analytics: comparing cloud data warehouses
BigQuery and Azure Synapse Analytics cloud data warehouses have the necessary features to support data analytics, but differ in aspects of architecture, pricing, and more.
Read now →
Feature
What's the difference between business intelligence and business analytics?
Find out how business intelligence and business analytics can help your enterprise make smarter, data-driven decisions.
Read now →
Feature
What is ELT? Understanding the difference between ELT and ETL
Cloud-based data warehouses and ELT deliver faster time to value than local hardware and ETL.
Read now →
Feature
How to use change data capture to optimize the ETL process
Businesses can optimize ETL by using change data capture to ingest only the data that has changed since the previous ETL operation.
Read now →
Feature
5 benefits of data analytics for your business
Data analytics can benefit your business by helping it reduce risks, improve its bottom line, and make informed decisions.
Read now →
Feature
Snowflake vs. BigQuery: comparing cloud data warehouses
Snowflake and BigQuery cloud data warehouses have great features to support data analytics, but differ in aspects such as architecture, pricing, and more.
Read now →
Feature
Snowflake Data Cloud: Revolutionizing data management and analytics
Snowflake is built for the cloud from the ground up. It delivers the flexibility and efficiency that simply isn’t possible with a traditional approach.
Read now →
Feature
What is a Data Lake? Examples & Solutions
A data lake is a centralized repository of raw, untransformed enterprise data. Today, most data lakes are implemented on cloud-based storage platforms.
Read now →
Feature
What is predictive analytics?
Predictive analytics applications parse complex data sets that integrate data from multiple sources to generate insights.
Read now →
Guide
Uncover business opportunities with Stitch Data and HubSpot
When you add HubSpot data to the Stitch data warehouse, you can learn more about your audience, access predictive analytics, and more — read on to learn more.
Read now →
Guide
How Can Google Analytics 4 Grow Ecommerce Websites?
A guide to why and how you should take advantage of new Google Analytics 4 functionality to grow your ecommerce site. Learn how to migrate historical data from an existing Universal Analytics property and pull new GA4 API data using Stitch.
Read now →
Feature
Building a data analytics stack for big data
Learn about the layers of the data analytics stack and how they can help your business unlock the value of big data.
Read now →
Feature
Data warehouse design for data-driven enterprises
Design a robust data warehouse by considering user needs, data modeling, the physical environment, and ETL tools.
Read now →
Feature
What is an enterprise data warehouse?
An EDW is a central repository that gathers enterprise data from multiple sources and makes it available for analysis, BI, and data-driven decision-making.
Read now →
General
Tutorial: Using Power BI with your data warehouse for analytics
To analyze data from diverse sources, you need a data warehouse that consolidates all of your data in a single location.
Read now →
Feature
What is big data analytics?
Big data analytics transforms digital information into useful business intelligence with software that makes sense of the endless stream of data a business receives.
Read now →
Feature
On-premises vs. cloud data warehouses: a comparison
Choosing a data warehouse depends on factors like cost, resources, control, scalability, and security that are unique to a business and its goals.
Read now →
Feature
How to connect a Singer tap with Stitch
We think it’s critical that ETL be extensible to support any data source. That's why we created the open source Singer project.
Read now →
Feature
Database vs. data warehouse: differences and dynamics
An introduction to the key differences between databases and data warehouses, two components of a data pipeline.
Read now →
Feature
Google BigQuery: a serverless data warehouse
Google BigQuery, a cloud-based data warehousing and analytics platform with a built-in query engine, can process terabytes of data in seconds.
Read now →
Feature
Business intelligence vs. data analytics
Data-driven organizations often use the terms "business intelligence" (BI) and "data analytics" interchangeably. They're not the same thing, but if someone asked you to explain the difference, what would you say?
Read now →
Feature
Data pipeline architecture: Building a path from ingestion to analytics
Data pipeline architecture is the design of processing and storage systems that capture, cleanse, transform, and route raw data to destination systems.
Read now →
Feature
MySQL: Get the best insights from your data, faster than ever
Drawing business insights from MySQL, the most popular open source RDBMS, requires an ETL solution.
Read now →
Feature
Business Intelligence - Your Complete Guide to BI Tools
Business intelligence (BI) is a collection of software tools and practices designed to leverage enterprise data to improve business decision-making.
Read now →
Feature
Business intelligence, data warehouses, and the cloud
Make faster, better decisions with business intelligence tools and a cloud data warehouse.
Read now →
Feature
3 advantages of self-service analytics
Self-service analytics tools allow nontechnical users to explore and share data, while maintaining necessary security protocols to protect sensitive information.
Read now →
Feature
Prescriptive Analytics Guide: Use Cases & Examples
Learn how prescriptive analytics can help your business learn how to make smarter decisions from its data
Read now →
Feature
An executive’s guide to data integration
Data integration is the process of consolidating and homogenizing data from disparate sources into a central location for data analysis and BI.
Read now →
Feature
What is Stream Processing?
Streaming data is a continuous flow of data from sources such as mobile apps, e-commerce websites, GPS devices, and IoT sensors.
Read now →
Feature
Top 24 tools for data analysis and how to decide between them
Take a look at the top tools for data analysis and learn how to choose one that fits your needs.
Read now →
Feature
Big data: a game-changer in every industry
Big data isn't just for search engines and media companies. Analyzing big data for insights can be a game-changer for any business.
Read now →
Feature
14 key metrics in Google Analytics for digital marketing
Learn about 14 Google Analytics metrics that all digital marketers should understand.
Read now →
Feature
Understanding ETL (extract, transform, load)
ETL (extract, transform, load) is a general process for replicating data from source systems to target systems to facilitate data analytics and BI.
Read now →
Feature
What is data consolidation?
Data consolidation is the corralling, combining, and storing of varied data in a single place to enable insights that drive better, faster decision-making.
Read now →
Guide
Social Media Data Extraction Across Entire Ad Stack
Automating social media data extraction allows advertisers to holistically track digital advertising performance and optimize their omnichannel ad strategy to drive leads faster.
Read now →
Feature
Using Python for ETL: tools, methods, and alternatives
Learn about libraries and frameworks for using Python to perform ETL, as well as alternative languages and tools to consider.
Read now →
Feature
What is a data warehouse? Your guide to definition, architecture, and benefits.
Learn about the role of a data warehouse in improving data accessibility and enhancing decision-making.
Read now →
Feature
A quick intro to Amazon QuickSight
Amazon QuickSight, a component of AWS, is a cloud-based business intelligence platform that allows users to create visualizations and dashboards.
Read now →
Feature
Data Strategy: What it is and how to achieve it
Data strategy refers to the tools, processes, and rules that define how to manage, analyze, and act upon business data.
Read now →
The data glossary
A definitive guide to data definitions and trends.
- Analytics
- Big data
- Business intelligence (BI)
- Common table expression
- Data analytics
- Data architecture
- Data engineering
- Data enrichment
- Data exploration
- Data ingestion
- Data integration
- Data lake
- Data migration
- Data mining
- Data modeling
- Data pipeline
- Data preparation
- Data science
- Data visualization
- Data warehouse
- Data Wrangling: Definition and Examples
- Database
- Deduplication
- ELT
- ESB - What is an Enterprise Service Bus?
- ETL
- ETL pipeline
- Information lifecycle management (ILM)
- Machine learning
- Master data management (MDM)
- SaaS
- Service-oriented architecture (SOA)
Looking for more?
Keep up with Stitch news and feature releases.
More on working with data
Stitch has a variety of guides to help you learn how to get the data from your various databases, SaaS tools, and other technologies into your warehouse manually. See these sample guides on sending MySQL to Amazon Redshift, Google AdWords to Google BigQuery, Jira to Postgres, and Salesforce to Snowflake.