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
Innovation
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.
Data-Focus
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
Compliance
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 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).
Compliance
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
Customer 360 For Digital Marketing Company
Discover how Valiance helped an Indian digital engagement solutions company with a 500 mn+ customer base increase its customer engagement through unstructured texts and ML algorithms
- The Key Challenge: Unstructured data made up most consumer interactions on the client's digital platform. These interactions alone created roughly 5 TB of data every month and contained a plethora of consumer lifestyle and preference data. The customer could not store and mine such datasets at scale. The customer also did not have any data science or big data expertise or experience.
Our Winning Moves
- The data engineering team created a Hadoop-based infrastructure to process TBs of unstructured data.
- The data science team created text mining rules and NLP algorithms to discover customer attributes. Based on this training sets were created. This means that using the discovered attributes, missing customer attributes can be predicted. Different algorithms were experimented with to arrive at winner algorithms.
- The business analytics and data science team also recommended additional customer attributes that could be useful from a marketing standpoint along with acting as a useful predictor in identifying missing ones. 450 customer attributes discovered were exposed to marketing team through open APIs.
- The results of text mining and machine learning were shared with the client and feedback was incorporated regularly.
- Mining unstructured data revealed customer qualities previously unknown. Intelligent marketing efforts had higher ROI than baseline.
- Two months of testing marketing initiatives increased digital client engagement by 10%.
Outcome :
Data Lake And Data Warehousing For B2B eCommerce On AWS Cloud
Find out how Valiance helped an Indian e-commerce business that connects manufacturers and suppliers with clients and offers B2C, B2B, and C2C sales services update data processing and reporting to generate daily or on-demand reports.
- The Key Challenge: The customer has over 500GB of data expanding at 20GB each month. The current Oracle RDBMS infrastructure manipulated data through stored procedures. The data was extracted manually through a WEB ERP system, and business reports were created in MS Excel. The reports based on monthly data provided a limited view of daily trends.
Our Winning Moves
- After reviewing the client's data configuration and technological roadmap, the data engineering team chose AWS Redshift for data warehousing.
- AWS Glue and QuickSight were suggested for a data pipeline and reporting solution.
- The team created an intermediate staging layer to gather input data with little alteration. This centralized historical data for subsequent reference.
- The team also logically viewed the data warehouse concept. AWS Redshift model with partition and sort keys were created from the permitted logical mode.
- Although data warehouses are meant to be "write once, read many," the client team was permitted to update specific entries infrequently.
- Multiple CSV files for each table were delivered at a pre-agreed S3 location.
- Glue tasks moved S3 data to the staging layer (AWS Redshift tables acting as temporary storage for daily processing).
- AWS Redshift queries in a cron job on an EC2 instance moved data from staging tables to warehouse tables. Job performance metrics and logs were kept in a metadata table and sent to the development team upon data pipeline completion or failure.
- We went live with the data warehouse in six months with three years of historical data.
- Incremental processes were set up to ingest data on an ongoing basis with an automated mechanism for handling failures.
Outcome :
