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Data Warehouse for
Business Intelligence
As a software architecture, the data warehouse has stood the test of time. The technology has evolved from the 1980s but the idea is still in its prime. It continues to fill an important business need, offering a window into the state of the enterprise.
Why Data Warehouse?
Enterprise data typically resides in silos across multiple systems like ERP, CRMs, and departmental applications, prohibiting a holistic view that management needs to make decisions. Data warehouse is an architecture specifically designed to support the data needs of decision makers. It consolidates data from disparate systems and organizes them in a manner that is easy to query and translate into insights. Using business intelligence (BI) tools connected to the data warehouse, business users can track KPIs in near real time and explore data in different hierarchical dimensions (drill-down, drill-across, slice-and-dice, etc.). Analysts can mine the data and identify significant trends and patterns.
- Data Consistency: Heterogeneous data structures are encoded consistently in the data warehouse. Users get uniform access to accurate and quality data.
- Data Reusability: Because data is stored at a granular level, different departments can flexibly reuse the data for different types of analysis.
- Diverse Analytics: From ad hoc queries on historical data to interactive querying, data warehouses can support a wide range of business intelligence needs.
- Decision Support: With up-to-date information at their fingertips, managers can make better decisions to improve both departmental and enterprise performance.
Data Warehouse Architecture
Traditional data warehouses follow a three-tier structure. The bottom tier consists of a staging store where data is brought in from different sources through the process of extract, transform and load (ETL). The middle tier consists of an OLAP server where data is preserved in a form suitable for analytical queries. Data here is nonvolatile and expected to be a single source of truth for the enterprise. The top tier consists of BI tools that make it easy for analysts to view and query information as reports or graphs.
Within the broad architecture mentioned above, there are many variations in approach. Extract, Load, Transform (ELT) approach delays transformation to the access stage. This enables users to flexibly prescribe the format of the target data.
Data Marts and Data Warehouse
While a centralized approach is easier to design and implement, it becomes less flexible and may become a bottleneck as the enterprise grows. A mixed approach with a central repository and department-specific data marts may be more suitable for large enterprises. The latter makes it easier for specific groups of users to access relevant departmental data quickly. Depending on business needs and architectural preferences, data marts can be placed above the middle tier or below. More complex structures are also adopted depending on specific user needs.
Cloud Data Warehouse
for Modern Enterprises
The cost and scalability challenges associated with running complex workloads on on-premise infrastructure is pushing the data warehouse to the cloud, which offers massively parallel computation and cost flexibility. Data is partitioned and stored on multiple nodes, each of which is capable of responding to queries. The response of each node is aggregated hierarchically and the end result is computed and presented. The cloud-based data warehouse architecture can be integrated with a variety of data visualization and machine learning tools to meet the advanced intelligence needs of the enterprise. Cloud data warehouse solutions such as Amazon Redshift, Azure Synapse Analytics, and Snowflake offer fault tolerance and infinite scalability.
Data Warehouse Development
Our goal is to architect a future-proof data warehouse solution that helps you harness the full value of your data for business improvement. Whether you are adopting the data warehouse architecture for the first time or migrating from a legacy data warehouse to cloud data warehouse, our team of business analysts, data engineers, and cloud consultants can successfully steer you through the process. By adopting DataOps practices, our team brings the required agility to your warehousing operations.
Stages of Data Warehouse Development
- 1Analyze: The first step is to establish the business objectives and determine the architecture requirements. The core business processes, dimensions, facts, and aggregation requirements as well as critical relationships within and between data entities are identified at this stage to create an effective data model for your data warehouse.
- 2Model: Star or snowflake design approach is adopted depending on data size and dimensions. Suitable mechanisms to handle the changing dimensions are devised. Depending on reporting requirements, the indexing approach is also decided; for example, column index is used for aggregation.
- 3Build: The technology stack is selected based on your existing infrastructure and future strategy. Our data engineering team creates the ETL pipelines for ingestion of transactional data into the data warehouse. Data compression and data partitioning approaches are decided and configured. Necessary views for business users are also created at this stage.
- 4Deploy: The solution is placed in the pre-production or production environment with important security controls. Based on user feedback, incremental improvements are made.
1Analyze
The first step is to establish the business objectives and determine the architecture requirements. The core business processes, dimensions, facts, and aggregation requirements as well as critical relationships within and between data entities are identified at this stage to create an effective data model for your data warehouse.
2Model
Star or snowflake design approach is adopted depending on data size and dimensions. Suitable mechanisms to handle the changing dimensions are devised. Depending on reporting requirements, the indexing approach is also decided; for example, column index is used for aggregation.
3Build
The technology stack is selected based on your existing infrastructure and future strategy. Our data engineering team creates the ETL pipelines for ingestion of transactional data into the data warehouse. Data compression and data partitioning approaches are decided and configured. Necessary views for business users are also created at this stage.
4Deploy
The solution is placed in the pre-production or production environment with important security controls. Based on user feedback, incremental improvements are made.
Technology Stack
Data Warehouse Vs Data Lake
The concept of data lake has gained much traction since the advent of cloud. Contrary to popular perception, it is not a replacement for a data warehouse. A data lake is a better solution when the primary need is to store a wide array of data with or without associated schema. A data warehouse is more than a storage solution as it delivers actionable insights for business.
| Data Warehouse | Data Lake | |
|---|---|---|
| Types of data supported | Structured data, particularly relational data | Structured, unstructured, semi-structured data |
| Schema | Specified at the time of write. Some systems support schema-on-read | Specified only at the time of read |
| Performance | Indexed data allows faster queries | Performance improvement through parallel operations |
| Users | Business analysts | Data scientists and business analysts |
Get corporate insights with a well-architected data warehouse.