Sifflet Description

Effortlessly monitor thousands of tables through machine learning-driven anomaly detection alongside a suite of over 50 tailored metrics. Ensure comprehensive oversight of both data and metadata while meticulously mapping all asset dependencies from ingestion to business intelligence. This solution enhances productivity and fosters collaboration between data engineers and consumers. Sifflet integrates smoothly with your existing data sources and tools, functioning on platforms like AWS, Google Cloud Platform, and Microsoft Azure. Maintain vigilance over your data's health and promptly notify your team when quality standards are not satisfied. With just a few clicks, you can establish essential coverage for all your tables. Additionally, you can customize the frequency of checks, their importance, and specific notifications simultaneously. Utilize machine learning-driven protocols to identify any data anomalies with no initial setup required. Every rule is supported by a unique model that adapts based on historical data and user input. You can also enhance automated processes by utilizing a library of over 50 templates applicable to any asset, thereby streamlining your monitoring efforts even further. This approach not only simplifies data management but also empowers teams to respond proactively to potential issues.

Sifflet Alternatives
DataBuck Reviews
DataBuck
(6 Ratings)
Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
Learn more
AnalyticsCreator Reviews
AnalyticsCreator
(46 Ratings)
Accelerate your data journey with AnalyticsCreator—a metadata-driven data warehouse automation solution purpose-built for the Microsoft data ecosystem. AnalyticsCreator simplifies the design, development, and deployment of modern data architectures, including dimensional models, data marts, data vaults, or blended modeling approaches tailored to your business needs. Seamlessly integrate with Microsoft SQL Server, Azure Synapse Analytics, Microsoft Fabric (including OneLake and SQL Endpoint Lakehouse environments), and Power BI. AnalyticsCreator automates ELT pipeline creation, data modeling, historization, and semantic layer generation—helping reduce tool sprawl and minimizing manual SQL coding. Designed to support CI/CD pipelines, AnalyticsCreator connects easily with Azure DevOps and GitHub for version-controlled deployments across development, test, and production environments. This ensures faster, error-free releases while maintaining governance and control across your entire data engineering workflow. Key features include automated documentation, end-to-end data lineage tracking, and adaptive schema evolution—enabling teams to manage change, reduce risk, and maintain auditability at scale. AnalyticsCreator empowers agile data engineering by enabling rapid prototyping and production-grade deployments for Microsoft-centric data initiatives. By eliminating repetitive manual tasks and deployment risks, AnalyticsCreator allows your team to focus on delivering actionable business insights—accelerating time-to-value for your data products and analytics initiatives.
Learn more
Decube Reviews
Decube
Decube is a comprehensive data management platform designed to help organizations manage their data observability, data catalog, and data governance needs. Our platform is designed to provide accurate, reliable, and timely data, enabling organizations to make better-informed decisions. Our data observability tools provide end-to-end visibility into data, making it easier for organizations to track data origin and flow across different systems and departments. With our real-time monitoring capabilities, organizations can detect data incidents quickly and reduce their impact on business operations. The data catalog component of our platform provides a centralized repository for all data assets, making it easier for organizations to manage and govern data usage and access. With our data classification tools, organizations can identify and manage sensitive data more effectively, ensuring compliance with data privacy regulations and policies. The data governance component of our platform provides robust access controls, enabling organizations to manage data access and usage effectively. Our tools also allow organizations to generate audit reports, track user activity, and demonstrate compliance with regulatory requirements.
Learn more
Validio Reviews
Validio
Examine the usage of your data assets, focusing on aspects like popularity, utilization, and schema coverage. Gain vital insights into your data assets, including their quality and usage metrics. You can easily locate and filter the necessary data by leveraging metadata tags and descriptions. Additionally, these insights will help you drive data governance and establish clear ownership within your organization. By implementing a streamlined lineage from data lakes to warehouses, you can enhance collaboration and accountability. An automatically generated field-level lineage map provides a comprehensive view of your entire data ecosystem. Moreover, anomaly detection systems adapt by learning from your data trends and seasonal variations, ensuring automatic backfilling with historical data. Thresholds driven by machine learning are specifically tailored for each data segment, relying on actual data rather than just metadata to ensure accuracy and relevance. This holistic approach empowers organizations to better manage their data landscape effectively.
Learn more

Integrations

View Integrations

Reviews - 1 Verified Review

Total
ease
features
design
support
See More Reviews Write a Review

Company Details

Company:
Sifflet
Headquarters:
United States
Website:
www.siffletdata.com

Media

Play Video Sifflet Screenshot 1 Sifflet Screenshot 2
Recommended Products
Our Free Plans just got better! | Auth0 Icon
Our Free Plans just got better! | Auth0

With up to 25k MAUs and unlimited Okta connections, our Free Plan lets you focus on what you do best—building great apps.

You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
Try free now

Product Details

Platforms
Web-Based
Types of Training
Training Docs
Live Training (Online)
Webinars
Training Videos
Customer Support
Online Support

Sifflet Features and Options

Data Quality Software

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

Data Lineage Tool

Database Change Impact Analysis
Filter Lineage Links
Implicit Connection Discovery
Lineage Object Filtering
Object Lineage Tracing
Point-in-Time Visibility
User/Client/Target Connection Visibility
Visual & Text Lineage View

Sifflet Lists

Sifflet User Reviews

Write a Review
  • Name: Anonymous (Verified)
    Job Title: Data Engineer
    Length of product use: 1-2 Years
    Used How Often?: Weekly
    Role: User
    Organization Size: 500 - 999
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    Enabler of Cross Platform Data Storytelling

    Date: Jun 25 2025

    Summary: In summary Sifflet has helped our organisation give full visibility of data lineage across multiple repos (separate dbt projects), and across platforms. We can see where data is coming from in Amazon S3, how it's being transformed as it moves through our dbt projects, and where it's being presented in our BI platform Quicksight.

    Positive: To call out some of the top features, they would be:-

    ✅ The ability to connect to multiple data sources; giving you great observability of data no matter what platform you use.

    ✅ The UI is clean, simple and easy to use. Setting up a new data source is easy, even uploading dbt manifest files via their API is a simple few commands.

    ✅ Their documentation on getting things set up and working is very easy to read; it’s not bloated and tells you exactly what you need to do.

    ✅Their communication with us has been a great experience. They’ve fixed bugs we’ve raised to them, informed us of new updates, and overall been very receptive of feedback.

    Negative: Sifflet are still developing some features, polishing existing ones and ironing out minor bugs (more like quality of life features). So there’s nothing major that would be a deal breaker.

    If I had to call out some points that need development they would be:-

    🤔 Their ‘Domain’ feature (ability to put data assets into domains, then limit users to a domain) is still in it’s basic form. It works, but needs some tweaks before it can be a real sellable feature.

    🤔Exploring the lineage of a very large lineage graph can be difficult due to the number of relationships/dependencies a model may have. This may be more of an issue with your own DAG architecture than Sifflet, but it’s worth keeping in mind if your models are inherently complex and coupled to one another. Thankfully, Sifflet are working on a new UI for their lineage graph and have demo'd it with us, so this should be a lot smoother in the near future.

    Read More...
  • Previous
  • You're on page 1
  • Next