Data Lake & Data Warehouse

Drive Intelligence, Innovate and Act Faster with a Centralized Scalable Repository that Unifies Multiple Truths and Versions of Structured and Unstructured Data. 

Data is used in practically every element of a business today, from product development to marketing, customer assistance, and more. While harvesting data is important, storing it in a safe, scalable, and highly accessible manner is an entirely different issue. Data warehouses and data lakes transform the noise generated by digital data into creative insights. By supporting big data volume and velocity, they inspire an enterprise-wide information revolution that streamlines data input, provides self-service capabilities, and lowers storage and compute costs.

At Valiance, we aim to tackle the productivity and scalability issues that restrict you from realizing the value of your data assets. We provide a solution that satisfies your present and future business requirements. To simplify data management and governance, our team will work with your current IT investments. They also connect operational stores and data warehouses, enabling you to enhance existing data applications.

Why Use Data Lakes


Harness a variety of data in one single repository to drive enterprise-wide innovation using AI & ML

Business Agility

Identify and pursue business growth opportunities faster with a holistic view of enterprise-wide data

Cost Efficiency

Save a variety of data, structured or unstructured, in flexible low-cost data storage for longer periods.


Democratize access to enterprise-wide data within the company driving increased productivity & innovation.

Our Capabilities

Integrate, standardize and prepare data sources for advanced analytics use cases

Create right sized, optimized & cost effective DL & DW solution with an eye for advanced analytics use cases

Advisory on Data governance, privacy and security for compliance needs

Expertise in open source & cloud native technologies for storage and data integration

Selection of right tools and platforms in partnerships with hyperscalers & ISV’s.

Managed services model to support upkeep and further enhancement of data platforms

Our Offerings

Ingestion and Integration 

Cleaning and Normalization

Data Access Control


Data Streaming

Data Warehouse Replacement

Data Organization and Search

Data Analytics

Ingestion and Integration

A data lake's landing zone is the optimal spot to combine data across systems in a consolidated repository. It can support a wide range of data ingestion pipelines, from file transfer protocol (FTP) upload to file sharing to relational databases. Connecting various data sources to a lake should be simple and intuitive. Integration tools offer quick connectivity to externally hosted services. 

Cleaning and Normalization

A lake's raw data can be delivered to a data transformation task for cleaning and processing. Custom business rules are frequently followed. De-duplication and normalization are two common use cases.

  • Data de-duplication: Avoids overcounting, erases all but one authoritative record of any information.
  • Normalization: The process of converting data such that it fits into the database's pre-defined structure. Strings are transformed to numbers, and timestamps to UTC dates.
  • Data Access Control

    Data lakes can be designed to provide multiple degrees of access to different stakeholders without duplicating the data.

    AWS Lake Formation has an extra security layer that allows for declarative management of access rights (for example, a lake administrator could have unrestricted access while a data analyst would be prevented from accessing personally identifying information (PII) of actual customers).


    Consumer privacy laws such as the GDPR and the CCPA allow customers the right to know what data is gathered (the right to know) and if it is to be destroyed (right to delete). A data lake, as the single repository for reporting and data integrations, is an effective regulatory compliance solution.

    Data Streaming

    Near-real-time data collecting for the Internet of Things is one of the most prevalent applications of data lakes (IoT). Streaming data can also be used to feed a data lake. Analytics pipelines designed with data lakes receive streams of video, audio, transaction logs, or communications. Streaming data can thus benefit from continuous reporting that can help with outlier detection for instance.

    Data Warehouse Replacement

    Data lakes are cheaper than warehouses and can replace them, especially for reporting and if the data is read-only. While data lakes did not previously enable transactional changes, governed tables are available today, and they enable transactions that allow users to enter, remove, and edit data, exactly like a database.

    Data Organization and Search

    Organizations with hundreds or thousands of data sources require a repository to store and handle all data. Data catalogs in data lakes offer table definitions, database names, and other information. Tags are useful information for tables and columns. Tag taxonomy searches data lake items (databases, tables, columns). Access permissions are also specified through tags.

    Data Analytics

    Data lakes are intended for big data analytics and, more importantly, real-time actions. Data lakes are well-suited to leverage large amounts of data consistently, with algorithms driving (real-time) analytics with rapid data. In addition, we see a movement in business intelligence towards in-data-lake BI solutions. Many firms are looking to implement a data lake with cloud migration.

    Our Success Stories

    Our Blogs

    Cracking the Code: Open vs Closed LLMs – Choosing the Right Fit for Your Business
    Streamlining Work with Generative AI: A Guide to Change Management
    Mastering Prompt Engineering: Key Practices to Enhance Knowledge Retrieval AI Apps

    Ready To Build a Scalable Data Infrastructure?

    Contact Us to Learn More