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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Progress MOVEit software is a Managed File Transfer (MFT) solution designed to help organizations securely control, monitor and govern the movement of sensitive and mission critical data. As file transfer volumes grow and regulatory requirements become more demanding, many organizations face operational risk from fragmented tools, limited visibility and manual processes. MOVEit software addresses these challenges by centralizing file transfer activity into a single, controlled environment that improves oversight, strengthens security and promotes operational consistency.
Progress MOVEit Transfer consolidates internal file transfers, external data exchanges and ad-hoc sharing into one managed system, reducing complexity while improving control over business critical data movement. The platform is well suited for organizations that handle sensitive data and operate in regulated environments, supporting requirements such as PCI DSS, HIPAA and GDPR.
Progress MOVEit Cloud extends these capabilities through a fully managed, auditor certified SaaS deployment that delivers the same security, control and governance standards as on premises deployments, without the overhead of maintaining infrastructure. The cloud service provides documented controls, strong encryption, detailed audit logging, role based access enforcement, built in resiliency and continuous monitoring.
Progress MOVEit Automation provides no code file transfer automation, simplifying complex, multi step file transfer processes that would otherwise rely on custom scripts. By reducing manual effort and standardizing file movement, MOVEit Automation helps minimize errors, accelerate partner onboarding and improve operational efficiency while maintaining strong security and compliance controls.
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StarTree
StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment.
StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark.
StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.
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UST IQ
UST IQ for AMI Analytics streamlines the entire data engineering process, managing everything from the ingestion of large-scale, high-frequency metering data to delivering comprehensive insights, allowing AMI business operations to prioritize essential decision-making over IT infrastructure concerns. It efficiently collects both real-time and historical data, including meter readings, events, alarms, GIS information, and external data sources, and transforms this information into query-ready formats using a cloud-native, microservices architecture. This setup supports self-service querying, location-aware and role-specific analytics, and proactive exception management, providing operations teams with crucial insights regarding network anomalies, meter performance, outages, and environmental data such as seismic activity or weather patterns. By doing so, it enhances the ability to optimize field crew deployment, avert expensive failures, and improve restoration efforts. The system processes vast quantities of data, handling hundreds of millions of records each day through low-latency micro-batching, typically in 5-minute intervals, while also offering features like 30-day rolling averages and alert-triggered notifications to further support operational efficiency. This comprehensive approach not only accelerates data processing but also ensures that actionable insights are readily available when needed, ultimately leading to improved operational effectiveness.
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