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20 Data Warehouse Best Practices

June 14th, 2024

52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.

Let’s explore how these best practices allow you to process increased volume, variety, and velocity of data, optimize data warehouse functionality and performance, and harness the power of data-driven insights.

Data Warehousing Best Practices

What Is a Data Warehouse?

A data warehouse is a centralized repository that stores and integrates data from multiple sources, such as operational systems, external databases, and web services. A data warehouse provides a consistent and consolidated view of data, regardless of where it originates from or how it is structured.

In other words, a data warehouse is organized around specific topics or domains, such as customers, products, or sales; it integrates data from different sources and formats, and tracks changes in data over time. The best part about a data warehouse is that it does not overwrite or delete historical data, simplifying data management significantly.

The key components of data warehouse architecture are:

  • Source systems: Source systems are responsible for generating and storing the raw data. We’re talking transactional databases, enterprise applications, and web services. These systems can be part of the company’s internal workings or external players, each with its own unique data models and formats.
  • Extract, Transform, and Load (ETL) process: ETL extracts data from source systems to transform it into a standardized and consistent format, and then delivers it to the data warehouse. This process ensures the data is clean, accurate, and in harmony with the data warehouse schema.
  • Data warehouse: The data warehouse is where the transformed and integrated data is stored. The data warehouse schema sets the rules, defining the structure with tables, columns, keys, and relationships. It doesn’t just store data but also metadata like data definitions, sources, lineage, and quality insights.
  • Data marts: Data marts (also called information marts) are tailored subsets of the data warehouse designed for specific business units, functions, or applications. They offer a more focused and customized view of the data, giving a performance boost to data analysis and reporting.
  • Data access tools: Data access tools let you dive into the data warehouse and data marts. We’re talking about query and reporting tools, online analytical processing (OLAP) tools, data mining tools, and dashboards. Data access tools enable users to analyze and visualize data in their own unique way.

Given the generally complex nature of the data warehouse architecture, there are certain data warehouse best practices that focus on performance optimization, data governance and security, scalability and future-proofing, and continuous monitoring and improvement.

Best Practices for Data Warehouses

Adopting data warehousing best practices tailored to your specific business requirements should be a key component of your overall data warehouse strategy. These strategies enable you to optimize query performance, fortify data security, establish robust data governance practices, and ensure scalability.

Performance Optimization

Boosting the speed and efficiency of data warehouse operations is the key to unleashing its full potential. Techniques like indexing, partitioning, caching, compression, and parallel processing play pivotal roles. Consider the following data warehouse best practices to enhance performance:

1. Strike the right balance with indexing to optimize query performance

Indexes are data structures that store the values of a specific column or a combination of columns, along with pointers to the rows that contain them. This data warehouse best practice allows you to speed up data warehouse data retrieval by reducing the number of disk I/O operations.

With indexing, your data warehouse no longer needs to scan the entire table, thus improving query performance. For instance, if you have a table with customer information, an index on the customer ID column will allow you to find a specific customer’s records quickly.

2. When partitioning, select the right column, align with query patterns, and avoid over-partitioning

Partitioning is the process of dividing a large table or index into smaller, more manageable units called partitions. Partitioning improves performance by reducing the amount of data that needs to be scanned, loaded, or updated at a time.

Partitioning can also enable other data warehouse best practices, such as parallel processing. For instance, if you have a table with sales data, you can partition it by date, region, or product category to optimize the queries that filter by these criteria.

3. Use caching to increase data access speeds

Caching is a crucial data warehouse best practice as it allows you to store frequently accessed data or query results in a temporary memory location, such as RAM or SSD. Caching can improve performance by reducing the latency and increasing the throughput of data warehouse operations. For example, if you have a query that calculates the total revenue for each month, you can cache the result and reuse it for subsequent queries that need the same information.

4. Use data compression to enhance storage efficiency

Compression allows you to reduce the size of data by applying some encoding or algorithm, such as dictionary encoding, run-length encoding, or delta encoding. Compression improves performance by saving disk space, reducing network bandwidth, and increasing the amount of data that can be processed in memory.

For instance, if you have a table with product information, you can compress the product description column by using a dictionary-based algorithm that replaces repeated words or phrases with shorter codes.

5. Accelerate query execution with parallel processing

Parallel processing helps execute multiple tasks or queries simultaneously by using multiple processors, cores, threads, or machines. Parallel processing improves data warehouse performance by distributing the workload and utilizing the available resources more effectively.

For example, if you have a query that joins two large tables, you can use parallel processing to divide the tables into smaller chunks and join them in parallel.

Data Governance and Documentation

Establishing and enforcing rules, policies, and standards for your data warehouse is the backbone of effective data governance and documentation. Techniques like metadata management, data cataloging, data lineage tracing, and data quality management are some of the data warehouse best practices you can incorporate.

6. Maintain a metadata repository to facilitate data discovery

Metadata Management allows you to define, collect, store, and maintain metadata, which is data about data. Metadata describes the structure, meaning, origin, and data usage. It is a valuable tool for managing and tracking changes and impacts.

Keeping track of metadata can help you understand the data, facilitate data integration, enable data lineage tracing, and enhance data quality. This not only aids user comprehension of data but also facilitates seamless data discovery, access, and analysis.

7. Use data cataloging for improved data accessibility

Data cataloging is a searchable and browsable inventory of the data assets in the data warehouse. It creates and maintains a metadata repository that describes the data sources, tables, columns, relationships, and business rules in the data warehouse.

Cataloging helps users access a centralized and searchable source of truth for data discovery, exploration, and understanding. If you have a data warehouse with multiple schemas, tables, and views, a data catalog is essential keep a unified and user-friendly interface for exploring and querying the data.

8. Use data profiling to ensure your data is healthy

Data profiling is the process of analyzing the data in the data warehouse to discover its characteristics, such as data type, format, range, distribution, frequency, uniqueness, completeness, accuracy, and relationships. It helps assess its structure, content, quality, and distribution, identify data anomalies and errors, and determine data cleansing and transformation requirements. Data profiling gives you an overview of the data health of a particular data asset.

9. Enhance data transparency with lineage tracing

Data lineage is the process of tracking your data’s origin, transformation, and destination in the data warehouse. This provides a clear and auditable record of data movement, flow, and impact.

Tracing data lineage helps understand the history and context of the data, verify the accuracy and reliability of the data, and troubleshoot data issues. For instance, if you have a table with sales data, you can use data lineage tracing to show the source systems, ETL processes, and intermediate tables that contributed to the data in the table.

10. Use relevant metrics to continuously monitor data quality

Data quality monitoring is the process of measuring, reporting, and improving data quality in the data warehouse. It helps measure and report data health based on predefined data quality metrics, such as accuracy, completeness, timeliness, validity, or uniqueness over time. With data quality monitoring, your team can be alerted to data anomalies errors, or changes after the data warehouse has been deployed.

Security Measures

Guarding your data warehouse against unauthorized access, modification, or disclosure requires robust security measures. Encryption, data masking, authentication, authorization, and auditing are your arsenal. Here are some data warehouse best practices to ensure data security:

11. Authorize, control, and monitor data access with role-based access control

Role-Based Access Control (RBAC) aligns access with user roles, ensuring individuals only access data and functions they need. Managing authorizations controls the level of access by defining what data or operations the users or applications can view, modify, or execute.

As a result, RBAC simplifies data security management and minimizes the risks of data breaches and leaks. For example, if you have a data warehouse with multiple users, you can use RBAC to assign different roles and privileges to the users, such as admin, analyst, or viewer, and restrict their access to specific schemas, tables, columns, or queries.

12. Protect sensitive information with data encryption

Encryption helps you transform the data or files into an unreadable form by using a secret key or algorithm. Encryption helps prevent data breaches, data theft, or data tampering by making the data inaccessible or meaningless to unauthorized parties.

For instance, if you have a table with sensitive data, such as customer SSN, addresses, or credit card numbers, you can encrypt the data before storing it in the data warehouse or transferring it over the network.

13. Use dynamic masking to selectively hide data

Data masking is the process of replacing the original data with fictitious or modified data that preserves the data format and functionality. It can protect the privacy and confidentiality of the data by hiding or obscuring the sensitive or identifying information.

For instance, if you have a table with customer information, you can mask the data by replacing the customer names with random names, the addresses with random addresses, or the credit card numbers with asterisks.

14. Manage data access with user authentication

Authentication is the process of verifying the identity of the users or applications that access the data warehouse. Authentication can prevent unauthorized access by ensuring that only legitimate and authorized parties can access the data warehouse. For example, if you have a data warehouse with multiple users, you can use authentication to require the users to provide their usernames and passwords, or other credentials, such as biometrics or tokens, before accessing the data warehouse.

15. Maintain accountability with regular audits

Auditing helps record and review the activities and events that occur in the data warehouse. It helps you monitor the data warehouse performance, usage, and security by providing logs, reports, and alerts on the data or operations that are accessed, modified, or executed. For example, if you have a data warehouse with multiple users, you can use auditing to track who, when, what, and how the users accessed or changed the data in the data warehouse.

Scalability and Future-Proofing

Ensuring your data warehouse can evolve with the future growth of data, users, and business requirements is essential. Techniques like capacity planning, modular design, and embracing cloud computing are your go-to strategies. Incorporate the following data warehouse design best practices:

16. Leverage cloud computing to handle large data sets

Cloud computing leverages remote servers and services to store, process, and analyze data. It offers scalability, flexibility, and cost-effectiveness by allowing the data warehouse to adjust the resources and services according to the demand dynamically and pay only for what is used.

For instance, if you have a data warehouse that needs to handle large and variable volumes of data, you can use cloud computing to store the data in scalable and distributed storage systems, such as Amazon S3 or Google Cloud Storage, and process the data in scalable and elastic compute platforms, such as Amazon Redshift or Google BigQuery.

17. Optimize resource allocation based on the workload

Capacity planning allows users to estimate and provision the resources and services needed to meet the current and future demands of the data warehouse. Capacity planning helps avoid performance degradation, resource wastage, or service interruption by ensuring the data warehouse has sufficient and optimal resources and services at all times.

For instance, if you have a data warehouse that needs to support a growing number of users and queries, you can use capacity planning to monitor and forecast the resource and service utilization, such as CPU, memory, disk, network, and concurrency, and plan for the resource and service allocation, upgrade, or migration accordingly. This avoids resource shortages, bottlenecks, or over-provisioning and ensures data availability and performance.

18. Select the right data warehouse modeling technique

Data warehouse modeling is the process of designing the logical and physical structure of the data warehouse, based on the business requirements and the data sources. Leveraging the appropriate schema, such as star or snowflake schema can help optimize your data warehouse for reporting.

It does so by organizing the data into facts and dimensions. Data warehouse modeling also involves applying various techniques, such as normalization, denormalization, aggregation, and partitioning, to optimize the data warehouse for performance, storage, and usability.

For instance, data warehouse modeling like star schema creates a central fact table that stores the measures of the business processes, and several dimension tables that store the descriptive attributes of the facts. This schema is simple, easy to understand, and fast to query, as it reduces the number of joins and tables involved.

However, the ideal data modeling technique for your data warehouse might differ based on your requirements. For instance, a star schema optimizes your data warehouse reporting, but it can also result in data redundancy, inconsistency, and update anomalies, as the same dimension attributes may be repeated in multiple tables.

19. Consider the modular design approach for scalability and performance

Modular design is a data warehouse design principle that advocates for breaking down the data warehouse into smaller, independent, and reusable modules. This approach can improve the scalability, maintainability, and performance of the data warehouse, as well as reduce the complexity and cost of development and testing.

An example of modular design is using a data vault architecture consisting of three types of tables: hubs, links, and satellites. Hubs store the business keys of the entities, links store the associations between the entities, and satellites store the attributes and history of the entities.

Each table is a module that can be loaded, updated, and queried independently without affecting the rest of the data warehouse. Like dimensional modeling, following the data vault design is ideal only in certain situations.

Learn more about whether you need a data vault.

Monitoring and Maintenance

The last in our list of data warehouse best practices is performance monitoring and periodic maintenance. The key to keeping your data warehouse running smoothly is to monitor its performance closely and fix any problems. It includes error handling, backup and recovery, and testing and debugging any changes you make.

20. Ensure smooth operations with continuous performance monitoring

Performance monitoring offers crucial insights into the bottlenecks, errors, and inefficiencies of operations.

Suppose you have a data warehouse that stores social media data for a digital marketing agency. You want to ensure that your it operates smoothly and reliably, delivering accurate and timely results to your users and clients. One way to do this is to implement performance monitoring in your data warehouse. It involves the following steps:

  • Define performance metrics: You need to define and measure the key performance indicators (KPIs) for your data warehouse, such as data load time, query response time, data quality, user satisfaction, etc. You may also use tools and techniques such as benchmarks, baselines, and thresholds to set and compare the performance standards and goals.
  • Collect performance data: You need to collect and store the performance data for your data warehouse, such as data volume, data velocity, data latency, data errors, data usage, etc. You may also use tools and techniques such as logs, alerts, and notifications to capture and report performance events and incidents.
  • Analyze performance data: You need to analyze and interpret the performance data for your data warehouse, such as identifying and diagnosing the root causes, impacts, and trends of performance issues and anomalies.


Astera: Empowering Data Warehouse Best Practices

Data warehouses are powerful and valuable assets for modern businesses. Incorporating data warehouse best practices into your data warehouse management ensures a well-rounded, high-performing, and secure environment, ready to meet the evolving demands of your business.

However, designing and building a data warehouse requires careful planning, implementation, and maintenance and must follow some best practices to ensure their functionality and performance.

A tool like Astera is indispensable for implementing data warehouse best practices as it addresses the complexities of data management, automates processes, ensures data quality, and provides the flexibility needed to adapt to evolving business requirements.

Why Choose Astera?

  1. Zero-Code ETL/ELT:
    • Benefit: Automate dataflow creation effortlessly for seamless population of database tables.
    • Impact: Streamline your processes, saving time and resources, while ensuring accuracy in your data transitions.
  2. Unified Metadata-Driven Solution:
    • Benefit: Design, develop, and deploy high-volume, operational data warehouses effortlessly.
    • Impact: Experience a unified approach to metadata-driven solutions, promoting consistency, and simplifying the development lifecycle.
  3. Built-In Validations and Checks:
    • Benefit: Ensure the integrity of your data and data model with built-in validations and checks.
    • Impact: Fortify your data quality, reducing errors and discrepancies, and fostering a foundation of trust in your data.
  4. Support for Different Schemas:
    • Benefit: Embrace flexibility with support for dimensional modeling, data vault 2.0, and 3NF schema.
    • Impact: Tailor your data warehouse to your unique needs, accommodating diverse schemas seamlessly.
  5. Data Modeler:
    • Benefit: Craft a data model from scratch or generate one for an existing database effortlessly.
    • Impact: Accelerate your data modeling process, promoting agility in adapting to evolving business requirements.
  6. Automated Data Pipelines:
    • Benefit: Facilitate automated data pipelines with a rich palette of 200+ transformations and robust scheduling.
    • Impact: Transform your data management landscape, ensuring a seamless and efficient flow from source to destination.
  7. Data Accuracy and Reliability:
    • Benefit: Ensure data accuracy and reliability through comprehensive validation tools.
    • Impact: Fortify your decision-making with trustworthy data, reducing the risk of errors and enhancing overall reliability.

Seize the Astera advantage and implement data warehouse best practices with the ease of no-code. Start your data warehousing journey with a free 14-day trial.

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