5.0 out of 5 stars
(15 Ratings)
Secure Eraser: Secure Data Deletion, Shredders Your Files & Folders.
Just because it has been removed from your hard drive doesn't mean that it is gone forever. Anyone can restore the information as long as it was not overwritten. It becomes more difficult if the computer has been resold, or given away.
Secure Eraser employs the most well-known method of data disposal. It overwrites sensitive information so that it cannot be recovered even with specialized software. Our award-winning solutions for permanently destroying data eliminate cross-references that may leave traces of deleted files within the allocation table of your hard disk.
This Windows software is easy to use and can overwrite sensitive data up to 35 times, regardless of whether they're files, folders or drives, recycle bins, or traces of surfing. You can also delete files that you have already deleted but not for good.
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4.6 out of 5 stars
(46 Ratings)
AnalyticsCreator is a metadata-driven design application for data warehouse automation and data product engineering across the Microsoft data stack.
Its Governed Control Model connects business meaning, data structures, transformation rules, dependencies, lineage and technical implementation in one controlled project model. Data teams design the required architecture in AnalyticsCreator, then generate native Microsoft assets from that design.
Generated outputs can include SQL Server objects, SSIS packages, Azure Data Factory pipelines, supported Microsoft Fabric components, deployment artefacts and Power BI semantic models. AnalyticsCreator supports dimensional, 3NF and hybrid modelling approaches together with ingestion, transformations, delta loading, historisation, Slowly Changing Dimensions, snapshots and repeatable data-processing patterns.
Because generated outputs are native Microsoft technology, no AnalyticsCreator runtime is required in production. Organisations retain ownership of the resulting implementation and can integrate generated assets into Git, Azure DevOps and CI/CD workflows.
Lineage, documentation and dependency information remain connected to the design, helping teams understand change impact before regenerating affected assets.
Design Intelligence extends this governed project context into AI-assisted data engineering by providing authorised AI tools and agents with structured access to metadata, lineage, dependencies and design rules.
Typical use cases include enterprise data warehouse development, Microsoft Fabric adoption, SQL Server and SSIS modernisation, governed Power BI delivery and repeatable data product engineering.
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OLake Go
OLake Go is a versatile open-source platform designed for the replication of operational data into data lakehouse environments. It provides connectivity to a wide array of databases, including PostgreSQL, MySQL, MongoDB, Oracle Database, SQL Server, and IBM Db2, along with integration capabilities for Apache Kafka and object storage solutions. The platform enables both full-load and incremental data ingestion, featuring change data capture for various supported connectors, and efficiently writes the ingested data to Apache Iceberg tables and Parquet files through compatible catalogs and storage targets. Data engineers and analytics teams can initiate ingestion tasks seamlessly via the OLake user interface or through command-line operations, and they have the flexibility to deploy the software within their own infrastructure to create synchronized datasets for further querying and analytics. With Docker-based deployment options and connector-specific configurations, teams can effectively manage their source connections, data destinations, and the overall data movement process in a self-hosted setting, thereby enhancing their operational efficiency. This empowers organizations to leverage their data more strategically while ensuring streamlined access to critical insights.
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Apache Iceberg
Iceberg is an advanced format designed for managing extensive analytical tables efficiently. It combines the dependability and ease of SQL tables with the capabilities required for big data, enabling multiple engines such as Spark, Trino, Flink, Presto, Hive, and Impala to access and manipulate the same tables concurrently without issues. The format allows for versatile SQL operations to incorporate new data, modify existing records, and execute precise deletions. Additionally, Iceberg can optimize read performance by eagerly rewriting data files or utilize delete deltas to facilitate quicker updates. It also streamlines the complex and often error-prone process of generating partition values for table rows while automatically bypassing unnecessary partitions and files. Fast queries do not require extra filtering, and the structure of the table can be adjusted dynamically as data and query patterns evolve, ensuring efficiency and adaptability in data management. This adaptability makes Iceberg an essential tool in modern data workflows.
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