Most businesses have data stored in a variety of locations, from in-house databases to SaaS platforms. To get a full picture of their finances and operations, they pull data from all those sources into a data warehouse or data lake and run analytics against it. But they don't want to build and maintain their own data pipelines.
Fortunately, it’s not necessary to code everything in-house. Here's an comparison of three such tools, head to head.
About Google Cloud Dataflow
Google Cloud Dataflow lets users ingest, process, and analyze fluctuating volumes of real-time data. It's one of several Google data analytics services, including:
- BigQuery, a cloud data warehouse
- Google Data Studio, a relatively simple platform for reporting and visualization
- Google Cloud Datalab, a more robust analytics tool that lets data professionals explore, analyze, transform, and visualize data and build machine learning models
- Google Cloud Dataprep, a data service for exploring, cleaning, and preparing structured and unstructured data
- Google Cloud Data Fusion, a cloud-native data integration service
Rivery is a cloud-based ETL platform.
Stitch Data Loader is a cloud-based platform for ETL — extract, transform, and load. More than 3,000 companies use Stitch to move billions of records every day from SaaS applications and databases into data warehouses and data lakes, where it can be analyzed with BI tools. Stitch is a Talend company and is part of the Talend Data Fabric.
|Focus||Stream and batch processing||Data ingestion, ELT||Data ingestion, ELT|
|Database replication||Full table; incremental replication via custom SELECT statements||Full table; incremental by field timestamps||Full table; incremental via change data capture or SELECT/replication keys|
|SaaS sources||None||Over 100||More than 100|
|Ability for customers to add new data sources||Yes||Yes||Yes|
|Connects to data warehouses? Data lakes?||Yes / Yes||Yes / Yes||Yes / Yes|
|G2 customer satisfaction||4.1/5||4.8/5||4.8/5|
|Purchase process||Self-service||Requires a conversation with sales||Options for self-service or talking with sales. Also available from the AWS store.|
|Compliance, governance, and security certifications||HIPAA||GDPR, SOC 2||HIPAA, GDPR, SOC 2|
|Data sharing||Yes||No||Yes, through Talend Data Fabric|
|Vendor lock-in||Annual or monthly contracts. No open source||Month to month or annual contracts. Open source integrations|
|Developer tools||Cloud Dataflow REST API, SDKs for Java and Python||REST API, RIvery API for external embedding||Import API, Stitch Connect API for integrating Stitch with other platforms, Singer open source project|
Let's dive into some of the details of each platform.
Google Cloud Dataflow
Cloud Dataflow provides a serverless architecture that can shard and process large batch datasets or high-volume data streams. The software supports any kind of transformation via Java and Python APIs with the Apache Beam SDK.
Rivery supports postload transformations defined in SQL via a feature called Logic Steps that can modify data within the target data warehouse.
Stitch is an ELT product. Within the pipeline, Stitch does only transformations that are required for compatibility with the destination, such as translating data types or denesting data when relevant. Stitch is part of Talend, which also provides tools for transforming data either within the data warehouse or via external processing engines such as Spark and MapReduce. Transformations can be defined in SQL, Python, Java, or via graphical user interface.
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Connectors: Data sources and destinations
Each of these tools supports a variety of data sources and destinations.
Google Cloud Dataflow
Cloud Dataflow supports both batch and streaming ingestion. For batch, it can access both GCP-hosted and on-premises databases. For streaming, it uses PubSub. Cloud Dataflow doesn't support any SaaS data sources. It can write data to Google Cloud Storage or BigQuery.
Rivery supports more than 100 integrations, to databases, SaaS applications, and storage platforms. Rivery provides a Custom API feature that lets users send data to Rivery using scripts they build internally. It supports seven destinations: Amazon Redshift, Google BigQuery, and Snowflake data warehouses; Amazon S3 and Microsoft Azure Data Lake; and Azure Blog Storage and Google Cloud Storage.
Stitch supports more than 100 database and SaaS integrations as data sources, and eight data warehouse and data lake destinations. Customers can contract with Stitch to build new sources, and anyone can add a new source to Stitch by developing it according to the standards laid out in Singer, an open source toolkit for writing scripts that move data. Singer integrations can be run independently, regardless of whether the user is a Stitch customer. Running Singer integrations on Stitch’s platform allows users to take advantage of Stitch's monitoring, scheduling, credential management, and autoscaling features.
Support, documentation, and training
Data integration tools can be complex, so vendors offer several ways to help their customers. Online documentation is the first resource users often turn to, and support teams can answer questions that aren't covered in the docs. Vendors of the more complicated tools may also offer training services.
Google Cloud Dataflow
Google provides several support plans for Google Cloud Platform, which Cloud Dataflow is part of. Documentation is comprehensive. Google offers both digital and in-person training.
Rivery provides support via Zendesk through a form on its website, and via email. Documentation is available from a link on the Rivery console. Rivery doesn't provide training services, but does offer video tutorials.
Stitch provides in-app chat support to all customers, and phone support is available for Enterprise customers. Support SLAs are available. Documentation is comprehensive and is open source — anyone can contribute additions and improvements or repurpose the content. Stitch does not provide training services.
Google Cloud Dataflow
Cloud Dataflow is priced per second for CPU, memory, and storage resources.
Rivery provides a 14-day free trial. Pricing isn't disclosed, but it is "based on usage, which is calculated by the frequency in which your data streams are updated, and the frequency in which data transformations are executed."
Stitch has pricing that scales to fit a wide range of budgets and company sizes. All new users get an unlimited 14-day trial. Standard plans range from $100 to $1,250 per month depending on scale, with discounts for paying annually. Enterprise plans for larger organizations and mission-critical use cases can include custom features, data volumes, and service levels, and are priced individually.
Get started now
Which tool is best overall? That's something every organization has to decide based on its unique requirements, but we can help you get started. Sign up now for a free trial of Stitch.