Telmai Description
A low-code no-code approach to data quality. SaaS offers flexibility, affordability, ease-of-integration, and efficient support. High standards for encryption, identity management and role-based access control. Data governance and compliance standards. Advanced ML models for detecting row-value data anomalies. The models will adapt to the business and data requirements of users. You can add any number of data sources, records, or attributes. For unpredictable volume spikes, well-equipped. Support streaming and batch processing. Data is continuously monitored to provide real-time notification, with no impact on pipeline performance. Easy boarding, integration, investigation. Telmai is a platform that allows Data Teams to detect and investigate anomalies in real-time. No-code on-boarding. Connect to your data source, and select alerting channels. Telmai will automatically learn data and alert you if there are unexpected drifts.
Telmai Alternatives
Metaplane
In 30 minutes, you can monitor your entire warehouse. Automated warehouse-to-BI lineage can identify downstream impacts. Trust can be lost in seconds and regained in months. With modern data-era observability, you can have peace of mind. It can be difficult to get the coverage you need with code-based tests. They take hours to create and maintain. Metaplane allows you to add hundreds of tests in minutes. Foundational tests (e.g. We support foundational tests (e.g. row counts, freshness and schema drift), more complicated tests (distribution shifts, nullness shiftings, enum modifications), custom SQL, as well as everything in between. Manual thresholds can take a while to set and quickly become outdated as your data changes. Our anomaly detection algorithms use historical metadata to detect outliers. To minimize alert fatigue, monitor what is important, while also taking into account seasonality, trends and feedback from your team. You can also override manual thresholds.
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Qualytics
Enterprises can manage their data quality lifecycle proactively through contextual data checks, anomaly detection, and remediation. Expose anomalies, metadata and help teams take corrective action. Automate remediation workflows for quick and efficient error resolution. Maintain high data-quality and prevent errors from impacting business decisions. The SLA chart gives an overview of SLA. It includes the total number SLA monitoring performed and any violations. This chart will help you identify data areas that require further investigation or improvements.
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Datafold
You can prevent data outages by identifying data quality issues and fixing them before they reach production. In less than a day, you can increase your test coverage for data pipelines from 0 to 100%. Automatic regression testing across billions upon billions of rows allows you to determine the impact of every code change. Automate change management, improve data literacy and compliance, and reduce incident response times. Don't be taken by surprise by data incidents. Automated anomaly detection allows you to be the first to know about them. Datafold's ML model, which can be easily adjusted by Datafold, adapts to seasonality or trend patterns in your data to create dynamic thresholds. You can save hours trying to understand data. The Data Catalog makes it easy to search for relevant data, fields, or explore distributions with an intuitive UI. Interactive full-text search, data profiling and consolidation of metadata all in one place.
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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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Company Details
Company:
Telmai
Headquarters:
United States
Website:
www.telm.ai/product-overview
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