Major Types of Data Warehouses that
We Build from the Ground Up
We deliver the different types of data warehouses to help organizations make an informed choice aligned with their data management strategies and analytical needs. Below you can overview the categories of data warehouses we generally create to get a grasp of their distinct characteristics, functionalities, and use cases.
Enterprise Data Warehouses (EDW)
These are comprehensive repositories that integrate data from multiple sources across an entire organization.They are designed to handle large volumes of data and support complex queries and analytics across different departments and functions.
Operational Data Stores (ODS)
They focus on real-time or near-real-time data integration and serve as a staging area for data before it is loaded into the data warehouse. Companies can use them for operational reporting, transactional processing, and supporting operational decision-making.
Data Marts
These are smaller, specialized data warehouses that focus on specific departments, business units, or functions within an organization. They are derived from the EDW and are designed for faster query performance and easier access to data for specific analytical purposes.
Analytical Data Warehouses
They are optimized for analytical processing and complex querying, store historical data and aggregate data from various sources to support advanced analytics, data mining, and predictive modeling. Companies can use them for in-depth analysis and reporting for strategic decision-making and business intelligence.
Cloud Data Warehouses
They are hosted on cloud platforms, eliminating the need for on-premises hardware and maintenance and providing on-demand resources for storing and analyzing data. They support distributed processing for handling large volumes of data and concurrent user queries.
Virtual Data Warehouses
They provide a logical layer that integrates data from disparate sources virtually, without physically moving or replicating data. Companies can leverage a unified view of data across different systems and platforms, enabling organizations to leverage existing data sources and infrastructure.
Data Governance
Data Virtualization
Infrastructure of our Data Warehouses Step by Step
Data warehouses that we develop consist of several key components that work together to store, manage, and analyze data efficiently. Let’s overview how they contribute to business data orchestration.
ETL (Extract, Transform, Load)
We set up extracting data from various sources, transforming it into a consistent format, and loading it into the data warehouse. Extraction involves retrieving data from source systems, transformation involves cleaning, integrating, and enriching the data, while loading involves inserting it into the warehouse.
Data Storage
We develop physical or virtual repositories where the structured, semi-structured, or unstructured data is stored. It often involves relational databases, columnar databases, or distributed file systems optimized for analytical queries.
Data Modeling
Our team designs the structure of the data warehouse, including defining tables, columns, relationships, and constraints. Dimensional modeling and star schema are common techniques used to organize data for efficient querying and analysis.
Metadata Repository
We create a repository that stores metadata, which provides information about the data stored in the warehouse. It includes details such as data definitions, data lineage, data quality rules, and data transformation logic, aiding in data governance and management.
Query and Analysis Tools
We implement query and analysis tools that provide interfaces for users to interact with the data warehouse, query the data, and generate reports and visualizations. These tools may offer features such as ad-hoc querying, OLAP (Online Analytical Processing), and data visualization.
Data Access Layer
Our team provides APIs, connectors, or interfaces for accessing data stored in the warehouse. It enables integration with external applications, BI tools, and analytics platforms, allowing users to retrieve and manipulate data for analysis and reporting.
Data Transformation Services
Security and Access Control
Our team implements security and access control mechanisms to enforce data privacy, integrity, and confidentiality within the data warehouse. This includes authentication, authorization, encryption, and auditing features to protect sensitive data and ensure compliance with regulations.
The Strategic Advantages of Data Warehouses for Business Operations
Our data warehouse development plays a central role in data-driven strategies by providing a unified repository for organizing and analyzing vast amounts of data. Learn how they drive the formulation and execution of data-driven strategies from the long-term benefits explained below.
Centralized Data Storage
Data warehouses that we develop provide a centralized repository for data from diverse sources, ensuring a unified and consistent view of organizational information. This facilitates informed decision-making by offering accurate and comprehensive data insights.
Fast Data Access
By streamlining data integration and standardization processes, data warehouses that we develop reduce errors, redundancy, and complexity, leading to faster access to critical information and accurate insights. This enables smoother operations and more efficient responses to business needs.
Data warehouses that we develop support advanced analytics, predictive modeling, and real-time reporting, enabling businesses to uncover valuable insights and trends. This empowers organizations to make proactive decisions and adapt quickly to changing market conditions.
Competitive Advantage
With access to timely and accurate data, businesses can develop data-driven strategies, analyze market trends, and innovate their processes. This fosters a competitive edge by enabling organizations to capitalize on opportunities, tailor products and services to customer needs, and stay ahead in dynamic markets.
Who we do it for
Our data warehouse development services offer versatile solutions that can be tailored to meet the standards and needs of various industries. Their scalability, flexibility, and capability to integrate data from different systems make them applicable across sectors, driving informed decision-making universally.
E-Publishing
E-publishing businesses can leverage our data warehouse development to store, organize, and analyze vast amounts of data related to content, readership, and sales. This will enable publishers to derive insights into reader preferences, market trends, and content performance, facilitating informed decision-making and targeted content strategies.
Our data warehouse development enable martech businesses to centralize and analyze customer data from various sources, such as CRM systems, analytics platforms, social media, etc. This facilitates targeted marketing efforts, personalized customer experiences, and data-driven decision-making for optimal marketing strategies.
Our data warehouse development provide fintech businesses with the capability to aggregate, analyze, and manage vast volumes of financial data in a single repository. They enable real-time insights into transactions, customer behavior, and market trends, facilitating risk management, fraud detection, compliance, and personalized financial services for customers.
Healthcare providers can leverage our data warehouse development to consolidate and analyze patient data from electronic health records and medical devices. This provides insights into patient outcomes, treatment effectiveness, and population health trends, facilitating personalized care, and healthcare management improvements.
Tech Stack
We typically employ components such as databases for storing structured and unstructured data, ETL tools for data integration, programming languages for scripting and automation, and analytics platforms for querying and analyzing data. Additional components may include data modeling tools, cloud services, and security frameworks.
Golang, Python, SQL, T-SQL
Frameworks and Libraries:
Django, Numpy, Pandas, Docker, Airflow, Jenkins, Grafana, Prometheus, Loki
Data Integration Platforms
Apache Kafka, Apache Airflow
ETL Tools
Microsoft SQL Server Integration Services (SSIS)
Databases and Storages
Microsoft SQL Server, Oracle, PostgreSQL, MySQL, SQLite, Redis, MongoDB, DynamoDB, AWS S3, ClickHouse, Amazon Redshift, Google BigQuery, Snowflake
Predictive analytics tools
LightGBM, XGBoost, CatBoost
BI tools
Power BI, Tableau
Our Portfolio
How We Work
As a full cycle software development company, we synchronize our development approach with our clients’ business goals and project nuances to deliver the right solution in the right time. Explore our engagement models, and choose which one works best.
The Lightpoint difference
Data warehouse development requires from the development team multiple skills, combining expertise in various domains to design, implement, and maintain robust data consolidation and analytics solutions. We believe that we have all the essential expertise required to build scalable, efficient, and reliable data warehouse systems.
Data Quality Management
Our knowledge of data quality principles, methodologies, and tools ensure that the data stored in the warehouse is accurate, consistent, complete, and reliable, including data profiling, cleansing, standardization, validation, governance, and monitoring.
Database Management and Optimization
We are proficient in database management systems (DBMS) and optimization techniques to efficiently store and manage large volumes of data within the warehouse, including indexing, partitioning, compression, and data lifecycle management.
Performance Tuning and Monitoring
Our skills in performance tuning and monitoring enable us to optimize the data warehouse's performance, identify bottlenecks, and ensure efficient query processing and system reliability, while also implementing data quality measures to maintain high-quality data throughout its lifecycle.
