Founded in 2018, we are a boutique data science firm focused on empowering businesses with Artificial Intelligence (AI) capabilities. We do whatever it takes to enable AI for your organization. Whether building data engineering pipelines, enabling cloud migration, developing data products to validate AI models, or performing QA, we provide the full spectrum of services.
As the name suggests, Cloud Data Warehouse is a Data Warehouse hosted in a cloud. A cloud data warehouse provides a centralized repository for storing and managing large amounts of structured unstructured data. It is used to store, process, and analyze data from multiple sources in a fast, scalable, and cost-effective manner.
Data engineering pipelines are systems that manage the movement of data from one or more Data Sources to Data Repository. The purpose of a data engineering pipeline is to automate the process of extracting data from source systems, transforming it into a usable format, and loading it into a data repository, such as a data warehouse or a data lake. The pipeline is monitored to ensure that data is being processed correctly and that any errors or issues are detected and addressed in a timely manner.
A data lake is a centralized repository that holds all the business data – structured and unstructured data – at any scale. Data lakes provide a high level of flexibility for the nature of data coming into it. The data lake keeps the data in its native format that you can later transform into something useful.
Data Lakehouse is a modern data architecture that combines the best of a data lake and a data warehouse. It provides a centralized repository for storing both structured and unstructured data at scale and allows organizations to perform analytics and run machine learning workloads on this data in near real-time.
Data Governance is the overall management of the availability, usability, integrity, and security of the data. It covers policies and procedures to ensure the effective use of data assets, establishing decision rights and accountabilities to ensure regulatory compliances.
Data lineage is the ability to trace the origin, history, and lineage of data through various stages of its lifecycle, from its initial creation to its final use. It helps organizations to understand where data came from, how it has been transformed, and how it is being used.
There are quite a few automated workflows for scheduling , triggering, monitoring etc. Workflow orchestration is the act of managing and coordinating the configuration and state of automated processes.
A feature store is a central repository in a modern data stack that stores and manages precomputed features, or transformations of raw data, for machine learning (ML) models. The purpose of a feature store is to provide a common interface for data scientists, engineers, and analysts to access, share, and reuse pre-processed data, reducing the time and effort required to build and train ML models
Reverse ETL is the process of moving data from a data warehouse into third-party systems to make data operational. It first extracts the data from a data warehouse or data lake, transforms it as necessary, and then loads it into a third-party SaaS application or platform.
Operational Analytics involves making the data available to operational teams – like sales, marketing, etc. – for functional use cases. Operational Analytics focuses on moving data out of the realm of pure analysis and into the on-the-ground operations of a business
Vertical Analytical Experiences are designed to enable business users to quickly and easily access the data and insights they need, without requiring specialized technical skills or deep knowledge of the underlying data platform. They often provide a simplified, intuitive user interface that is easy to navigate, and they are pre-configured with industry-specific metrics, data models and business logic.
A Data Catalog is tool that allows organizations to discover, understand, and manage the data that they have. It typically includes metadata. It helps you find the right data asset for the problem.
Data observability uses automation to monitor data quality to identify the potential data issues before they become business issues. It empowers data engineers to monitor the data and quickly troubleshoot any possible problem
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