K2View vs. Sifflet Comparison


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About At K2View, we believe that every enterprise should be able to leverage its data to become as disruptive and agile as the best companies in its industry. We make this possible through our patented Data Product Platform, which creates and manages a complete and compliant dataset for every business entity – on demand, and in real time. The dataset is always in sync with its underlying sources, adapts to changes in the source structures, and is instantly accessible to any authorized data consumer. Data Product Platform fuels many operational use cases, including customer 360, data masking and tokenization, test data management, data migration, legacy application modernization, data pipelining and more – to deliver business outcomes in less than half the time, and at half the cost, of any other alternative. The platform inherently supports modern data architectures – data mesh, data fabric, and data hub – and deploys in cloud, on-premise, or hybrid environments.	About Automatically cover thousands of tables with ML-based anomaly detection and 50+ custom metrics. Comprehensive data and metadata monitoring. Exhaustive mapping of all dependencies between assets, from ingestion to BI. Enhanced productivity and collaboration between data engineers and data consumers. Sifflet seamlessly integrates into your data sources and preferred tools and can run on AWS, Google Cloud Platform, and Microsoft Azure. Keep an eye on the health of your data and alert the team when quality criteria aren’t met. Set up in a few clicks the fundamental coverage of all your tables. Configure the frequency of runs, their criticality, and even customized notifications at the same time. Leverage ML-based rules to detect any anomaly in your data. No need for an initial configuration. A unique model for each rule learns from historical data and from user feedback. Complement the automated rules with a library of 50+ templates that can be applied to any asset.
Platforms Supported Windows Mac Linux Cloud On-Premises iPhone iPad Android Chromebook	Platforms Supported Windows Mac Linux Cloud On-Premises iPhone iPad Android Chromebook
Audience Data engineers, data architects, data scientists, data testers, CIOs and business leaders	Audience Teams and companies requiring a solution to monitor their data assets, metadata, and infrastructure
Support Phone Support 24/7 Live Support Online	Support Phone Support 24/7 Live Support Online
API Offers API	API Offers API
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing No information available. Free Version Free Trial	Pricing No information available. Free Version Free Trial
Reviews/Ratings Overall 3.0 / 5 ease 4.0 / 5 features 5.0 / 5 design 5.0 / 5 support 4.0 / 5 Read all reviews	Reviews/Ratings Overall 5.0 / 5 ease 5.0 / 5 features 4.5 / 5 design 5.0 / 5 support 4.0 / 5 Read all reviews
Pros & Cons from Real Users Pros 1.Great integration fast 1-2 months instead of a year . 2.good performance due to clustering(cassandra) 3.In memory data management 4. Standard SQL to query data 5.Good scalability 6.New and efficient ETL solution Cons 1.solution cost at least 1M $! 2.k2view uses it's own deployment team.	Pros & Cons from Real Users Pros AI-based anomaly detection works flawlessly Easy data lineage visualization Customizable metrics for business-specific monitoring Smooth cloud integration 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. Cons Mobile interface could be slightly improved Initial setup requires some familiarization for complex pipelines 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.
Training Documentation Webinars Live Online In Person	Training Documentation Webinars Live Online In Person
Company Information K2View Founded: 2009 United States k2view.com	Company Information Sifflet United States www.siffletdata.com
Alternatives DATPROF	Alternatives Aggua
Accelario	Decube
IRI DarkShield IRI, The CoSort Company	Matia
Delphix Perforce	dbt dbt Labs
dbt dbt Labs View All	Validio View All
Categories Application Modernization Cloud Migration Customer Data Platforms (CDP) Data Catalog Data Engineering Data Fabric Data Governance Data Integration Data Management Data Masking Data Pipeline Data Preparation Data Privacy Management Data Subject Access Request (DSAR) Data Virtualization DataOps GDPR Compliance Integration iPaaS Master Data Management Metadata Management Sensitive Data Discovery Synthetic Data Generation Test Data Management	Categories Data Catalog Data Engineering Data Lineage Data Observability Data Quality DataOps Observability
Show More Features Data Fabric Features Data Access Management Data Analytics Data Collaboration Data Lineage Tools Data Networking / Connecting Metadata Functionality No Data Redundancy Persistent Data Management Data Management Features Customer Data Data Analysis Data Capture Data Integration Data Migration Data Quality Control Data Security Information Governance Master Data Management Match & Merge Integration Features Dashboard ETL - Extract / Transform / Load Metadata Management Multiple Data Sources Web Services
Integrations Amazon EMR Amazon QuickSight Amazon Redshift Amazon S3 Apache Spark Azure Marketplace Datadog Firebolt Looker Microsoft Power BI Microsoft Teams Opsgenie PostgreSQL Prefect Presto SQL Server Salesforce Slack Tableau Show More Integrations View All 3 Integrations	Integrations Amazon EMR Amazon QuickSight Amazon Redshift Amazon S3 Apache Spark Azure Marketplace Datadog Firebolt Looker Microsoft Power BI Microsoft Teams Opsgenie PostgreSQL Prefect Presto SQL Server Salesforce Slack Tableau Show More Integrations View All 34 Integrations
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