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DataMig enables you to successfully migrate data warehouse to any cloud at 40% lesser cost and reducing the development lifecycle by up to 60%.
SnowDash enables you to successfully monitor Snowflake usage across multiple departments within an organisation on any Cloud . SnowDash provides view across multiple vendors who are implementing / maintaining Snowflake for the customer thereby reducing the governance slippage by up to 60%. This solution jointly developed with Snowflake.
Inaccurate and inconsistent data can negatively impact decision-making, hinder business opportunities, damage customer experiences, falter on regulatory compliances as well as financial repercussions.
Timely and accurate data is crucial for making informed decisions and driving business activities and organizations need to perform data reconciliation regularly as part of their business-as-usual activities. However, Data Reconciliation remains a time-consuming, manual effort full of risks for most of the Organizations.
ℼ-Recon is a propriety accelerator solution that centralises data from disparate systems and ensure quality, consistency, and accuracy across business units. It is highly effective in automating validation and testing of Database, DW migration projects and ensuring greater Data Quality, higher performance, and greater productivity
Organizations wanting to modernize their IT landscape and adopt Snowflake to meet their growing analytics needs, rely on the costly vendor connectors to build data applications and pipelines to quickly activate and Ingest data without any coding.
ℼ-Ingest provides multiple pre-built integrations with a variety of sources such as Databases (RDBMS , noSQL), CRM & ERP systems, Social Media platforms, Cloud Object Storage Systems, Advertisement Platforms (Facebook, Yahoo Ads), and more, thereby simplifying the integration, transformations, and processing of disparate data.
Designed with the ability to create realistic, privacy-compliant synthetic datasets tailored to unique project needs. It enables seamless generation of high-quality synthetic data that mirrors the structure and statistical properties of real-world data without exposing sensitive information. It supports complex object definitions, configurable data patterns, and relationship modelling, making it ideal for machine learning, software testing, and analytics across diverse domains.