Empower your enterprisw strategy with robust data-driven decisions. Navigate the complexities of Cloud with experts.
Data Warehouse Services
Data Warehouse As a Service
- Data warehouse configuration and development.
- Data warehouse integration with the existing infrastructure (data sources, BI and data analytics infrastructure).
- Data migration and data cleaning.
- Continuous support and administration of the data warehouse.
- On-demand data warehouse configuration.
Data Warehouse Advisory Services
Our data warehouse advisory services may include:
DWH solution design:
- DWH requirements engineering.
- Business case creation.
- DWH solution architecture.
- DWH tech selection, outline of the optimal cloud data warehouse platform and its configuration*.
- Data governance design for data quality, availability, and security.
- Data modeling, ETL/ELT design, etc.
DWH implementation/migration/optimization plan.
Consulting support or complete project management.
Data Warehouse Implementation
- Data warehouse requirements engineering.
- Data warehouse solution conceptualization and platform selection.
- Data warehouse solution architecture design.
- Data warehouse system analysis.
- Data modeling and ETL/ELT design.
- Data warehouse solution development.
- Data warehouse quality assurance and launch.
- Data warehouse after-launch support.
Data Warehouse Migration
- Outlining a migration strategy and a plan.
- Designing a cloud data warehouse architecture.
- Assisting in selecting the right cloud vendor*.
- Configuring the cloud cluster in a way to optimize costs.
- Redeveloping a data warehouse on a new platform.
- Integration of cloud and on-premises environments.
- Transferring both master data and metadata to the new data warehouse.
- Testing the completeness of data to ensure the migration’s success.
Data Warehouse Testing
- Studying project requirements.
- Test planning and test design.
- Test implementation.
- Result analysis and accountability.
Data Warehouse Support
- DWH solution architecture optimization.
- Optimization of individual DWH tools (keeping more data in memory, adding indexes to tune query performance).
- DWH design optimization (changing database schemas, data loading, etc.).
Enterprise Data Warehouse Types
There are three deployment environment types for data warehousing solutions:
- On-premises – a company purchases all required hardware and software to build and deploy an EDW and maintains it further on.
- Cloud-hosted – a company deploys an EDW in the cloud, eliminating the need to purchase and maintain hardware and software.
- Hybrid – a company augments an on-premises enterprise data warehouse with a cloud-hosted repository.
On-premises
Pros:
- Full control over the enterprise data warehouse. In case of a failure, an in-house IT team has direct access to the DWH’s problem area for hardware and software tuning. Moreover, data security remains strictly under the in-house IT team’s control.
- Full compliance with the required data standards. Data security compliance is easier to achieve with on-premises enterprise DWHs.
- Availability. Business users from a facility where the EDW is located can effectively access all the data stored in the data warehouse without dependence on the internet connection.
Cloud
Pros:
- Scalability. The inherent agility of cloud data warehouses allows upscaling and downscaling with no impact on enterprise data warehouse performance.
- Reduced costs. There are no hardware-related costs (hardware acquisition, deployment, maintenance, administration, etc.). And if you opt for Enterprise Data Warehouse as a Service, all software acquisition and maintenance costs are eliminated too.
Hybrid
Pros:
- Cloud flexibility. Meeting storage and compute requirements with near-unlimited cloud resources.
- Data compliance. Ensuring sensitive data is stored within an environment that fully meets data compliance standards.
Get a Free Expert Guide to Choosing an EDW Platform
Pros:
To help our customers eliminate the guesswork when selecting a cloud data warehouse provider, Sneak Peek Tech’s consultants prepared a comparative guide containing:
- Main factors to consider before choosing a DWH platform: e.g., the target data volume, data types to store, data transformation and cleansing needs, etc.
- Best practices on DWH planning so that the analytical solution brings maximum efficiency in the long run.
- Recommendations and precautions on particular cloud products.
Key EDW Integrations
Data Lake
To store massive volumes of heterogeneous data imported from multiple sources in its original format. In data lakes, the captured raw data remains secure and accessible for further analytics querying and reporting with EDW.
Self-Service Analytics Software
To enable business users to make decisions based on timely and relevant reports, queries, and analysis customized and conducted according to their own needs.
Machine Learning Software
To enable data scientists to build machine learning models with processed and cleaned data from the EDW system to predict a company’s revenue, assess financial risks, forecast market trends and the company’s performance, etc.
Need to Consolidate Your Corporate Data?
Enterprise Data Warehouse Benefits
Cross-Department Collaboration
With all business users having access to a single point of truth, EDW eliminates data silos, enables easy and secure data sharing, and guarantees that all users make decisions based on common information.
Saved Time of IT Staff & Data Analysts
Thanks to self-service BI capabilities and data management automation, an EDW minimizes the involvement of IT teams in BI tasks and significantly reduces the manual work of analytics teams.
A 360-Degree View
The role of an enterprise warehouse in data mining is essential. An EDW consolidates and stores massive data from various sources, allowing companies to get a comprehensive view of their business at the current point, from the perspective of the past years, and at an angle of intelligent predictions and what-if scenarios.
A Solid Foundation For Implementing AI/ML Analytics
Providing vast amounts of multi-source, high-quality data, an EDW makes it possible to train precise AI/ML models that enable accurate predictions, smart recommendations, and business process automation.
Sample Architecture of a Real-Time Data Warehouse
The ‘real-time’ in a real-time data warehouse implies that the analytics is performed within a short time frame (from milliseconds to minutes) after the new data arrives, depending on the specific business needs and solution complexity. Below, Sneak Peek Tech’s data engineers provide an example of a high-level real-time data warehouse architecture.
Key Processes That Happen in an RTDW
Data Ingestion
An RTDW ingests real-time data with high throughput performance. Depending on the data source type and the physical distance between the data source and the analytics software, data can be ingested into the processing block by several means:
- Direct connections: for IoT systems.
- APIs: for third-party data sources (e.g., payment gateways, messaging services, authentication services).
- A message bus: for corporate systems (ERP, CRM, accounting software, etc.) and third-party services (e.g., customer data from an e-commerce platform, telematics data from a third-party device provider).
Real-Time Storage
The real-time storage acts as a buffer that ensures reliable queuing logic, e.g., record ordering, scaling resources, delivering messages with minimal latency. This location also enables pre-analytics processing (ETL/ELT).
Real-time Processing & Analytics
Most RTDW solutions rely on AI to enhance real-time streaming data analysis and provide intelligent insights on events as they happen. The software instantly notifies users about the events that require manual settlement and can automatically trigger immediate actions (e.g., block a credit card in case of fraud detection or stop the machine that reported a critical event). AI-powered predictive analytics enables accurate forecasting of the required metrics, while prescriptive analytics offers intelligent recommendations on the proper actions.
Data Access & Reporting
An RTDW makes the processed data immediately available as short-term insights and event-based alerts or automated action triggers. In addition, such solutions enable comprehensive analytics of the accumulated historical data and ad hoc generation of custom reports.
Choose Your Service Option
Data Warehouse Implementation / Migration / Optimization Consulting
We offer advisory support or complete project management to help you:
- Implement a cost-effective DWH solution under set time and budget.
- Migrate your legacy DWH solution to the cloud to achieve dynamic scaling of the DWH infrastructure and optimize DWH performance and costs.
- Upgrade the existing DWH solution to meet new business needs (e.g., add real-time analytics).
End-To-End Data Warehouse Implementation
We help you:
- Consolidate disjointed data sources into centralized storage.
- Ensure uninterrupted data flow via planning and implementing the required integrations with other systems (e.g., with an enterprise data lake).
- Achieve high data quality.
- Ensure data security and compliance.
Data Warehouse Support & Evolution
We help you meet newly arising analytics needs by:
- Reducing data latency.
- Solving performance and concurrency problems.
- Lowering storage and processing costs.
- Achieving DWH stability.
- Ensuring timely and quality data flow for business users with near-zero DWH downtime.
Providing complimentary services (e.g., AI/ML services, data lake consulting, BI and visualization services).
Technologies We Use
Cloud data storage
Data warehouse technologies
Data integration
Data visualization
Big data
Machine learning platforms and services
Machine learning frameworks and libraries
Frameworks
Libraries
Cloud services
Cloud data storage
Data warehouse technologies
Data integration
Data visualization
Big data
Machine learning platforms and services
Machine learning frameworks and libraries
Frameworks
Libraries
Cloud services