Great Expectations Description
Great Expectations serves as a collaborative and open standard aimed at enhancing data quality. This tool assists data teams in reducing pipeline challenges through effective data testing, comprehensive documentation, and insightful profiling. It is advisable to set it up within a virtual environment for optimal performance. For those unfamiliar with pip, virtual environments, notebooks, or git, exploring the Supporting resources could be beneficial. Numerous outstanding companies are currently leveraging Great Expectations in their operations. We encourage you to review some of our case studies that highlight how various organizations have integrated Great Expectations into their data infrastructure. Additionally, Great Expectations Cloud represents a fully managed Software as a Service (SaaS) solution, and we are currently welcoming new private alpha members for this innovative offering. These alpha members will have the exclusive opportunity to access new features ahead of others and provide valuable feedback that will shape the future development of the product. This engagement will ensure that the platform continues to evolve in alignment with user needs and expectations.
Great Expectations Alternatives
dbt
dbt Labs is redefining how data teams work with SQL. Instead of waiting on complex ETL processes, dbt lets data analysts and data engineers build production-ready transformations directly in the warehouse, using code, version control, and CI/CD. This community-driven approach puts power back in the hands of practitioners while maintaining governance and scalability for enterprise use.
With a rapidly growing open-source community and an enterprise-grade cloud platform, dbt is at the heart of the modern data stack. It’s the go-to solution for teams who want faster analytics, higher quality data, and the confidence that comes from transparent, testable transformations.
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DataHub
DataHub is a versatile open-source metadata platform crafted to enhance data discovery, observability, and governance within various data environments. It empowers organizations to easily find reliable data, providing customized experiences for users while avoiding disruptions through precise lineage tracking at both the cross-platform and column levels. By offering a holistic view of business, operational, and technical contexts, DataHub instills trust in your data repository. The platform features automated data quality assessments along with AI-driven anomaly detection, alerting teams to emerging issues and consolidating incident management. With comprehensive lineage information, documentation, and ownership details, DataHub streamlines the resolution of problems. Furthermore, it automates governance processes by classifying evolving assets, significantly reducing manual effort with GenAI documentation, AI-based classification, and intelligent propagation mechanisms. Additionally, DataHub's flexible architecture accommodates more than 70 native integrations, making it a robust choice for organizations seeking to optimize their data ecosystems. This makes it an invaluable tool for any organization looking to enhance their data management capabilities.
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DataBuck
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.
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Anomalo
Anomalo helps you get ahead of data issues by automatically detecting them as soon as they appear and before anyone else is impacted.
-Depth of Checks: Provides both foundational observability (automated checks for data freshness, volume, schema changes) and deep data quality monitoring (automated checks for data consistency and correctness).
-Automation: Use unsupervised machine learning to automatically identify missing and anomalous data.
-Easy for everyone, no-code UI: A user can generate a no-code check that calculates a metric, plots it over time, generates a time series model, sends intuitive alerts to tools like Slack, and returns a root cause analysis.
-Intelligent Alerting: Incredibly powerful unsupervised machine learning intelligently readjusts time series models and uses automatic secondary checks to weed out false positives.
-Time to Resolution: Automatically generates a root cause analysis that saves users time determining why an anomaly is occurring. Our triage feature orchestrates a resolution workflow and can integrate with many remediation steps, like ticketing systems.
-In-VPC Development: Data never leaves the customer’s environment. Anomalo can be run entirely in-VPC for the utmost in privacy & security
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Integrations
Company Details
Company:
Great Expectations
Website:
greatexpectations.io
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support
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