With organizations increasingly relying on data-driven insights for informed decision-making, data staging practices have become essential. From enhancing your organization’s data management process to improving analytics, data staging plays a critical role.
Let’s look into the details of data staging, its benefits, and the best practices to help you leverage the maximum benefits of the process.
What is Data Staging?
Data staging is the intermediate step in data warehousing and business intelligence, where data is collected, transformed, and prepared for analysis. The raw data extracted from various sources is staged or stored temporarily in a data staging area, allowing for further processing or transformation.
The main purpose of data staging is to ensure that clean, consistent, and ready-to-analyze data enters the data warehouse or any other analytics system. This helps derive quality insights from the data for improved decision-making.
Depending on your organization's strategy, the exact series of steps in data staging will vary. The staging may occur in or outside the data warehouse based on business requirements and the design of a data warehouse. However, you may choose to have staging areas in both locations.
What is a Data Staging Area?
A data staging area is an intermediate storage area where data from source systems is temporarily stored for additional processing. It is located between the data sources and data targets, typically data warehouses or data marts.
Data staging spaces are often transient in nature, with their contents being erased after the data is moved to the desired repository. However, you can also have staging area architectures designed to hold data for long periods of time for debugging or preservation purposes.
A data staging area can be external or internal, depending on whether the data is outside a warehouse or inside a warehouse.
What are the types of Data Staging?
- External Staging
- Internal Staging
The traditional data staging area is outside a warehouse, typically in a cloud storage provider, like Amazon Web Simple Storage Solution (AWS S3) or Google Cloud Storage (GCS).
In external staging, a data engineer loads data from external sources and performs simple transformations before loading the data into a warehouse. The staged data is often stored in a raw format like Parquet or JSON, either compressed and optimized specifically for staging or determined by the source.
External data staging is beneficial in cases where:
- Real-time or event streaming data must be transformed.
- The volume of raw data exceeds the reasonable storage capacity of a data warehouse.
- Depending on the existing data, operations such as MERGE or UPSERT must be performed.
Internal staging is a data staging method in which the staging area is located within the data warehouse. Unified cloud data warehouses often utilize a separate, internal staging process.
In internal staging, the raw data is loaded directly into the data warehouse. This can be before or after any transformation takes place. Generally, in internal staging, the transformation of data occurs after it is loaded into the data warehouse. This helps leverage the capabilities and computational power of the data warehouse for performing complex transformations.
The raw data, typically stored in a separate area from the rest of the warehouse, is transformed, cleaned, and normalized in the staging area. This is done without affecting the already structured and processed data in the other parts of the warehouse. The cleaned and prepared data helps reduce complexity and mitigate data sprawl for data teams to present to stakeholders.
Internal staging helps:
- Transform tables with additional functions such as ordering, grouping, numbering rows, and running totals. This is handy when additional information is required within the same dataset.
- Index target tables for efficient queries and re-access.
Why is Data Staging Important?
Data staging plays a crucial role in data management and business analytics. Here are some of the key reasons that make data staging important:
- Data Quality and Consistency: The data transformation and cleansing involved in data staging help maintain data quality and consistency. It ensures that the data used for analysis by your organization is accurate, reliable, and consistent, making it easier to perform analysis and derive insights.
- Complex Data Integration: Data staging provides a platform to integrate the diverse data extracted from multiple sources into a uniform format. This results in a comprehensive view of your business’ data for easier analysis.
- Facilitates Advanced Analytics: Properly cleansed and formatted data makes it easier for you to achieve advanced analytics and effectively leverage BI tools.
- Data Security and Governance: When data is staged in a controlled environment, you can implement security measures and governance policies. This protects sensitive data and ensures compliance with data privacy regulations.
What are the benefits of a Data Staging Area?
Data staging, being an integral part of the ETL process, offers numerous benefits for enhanced data management and analysis. Some benefits include:
- Backup: With backups, you can store, compress, and archive data all the way to the database level. Data backup is usually an issue when large volumes of data are sent. The solution to this is to send data in pieces that can be readily preserved and archived using staging areas.
- Recovery Points: If your data gets corrupted at any point, you can go back to recovery points that are created during the data staging process and reload the data.
- More Control: A data staging zone gives you more control, allowing you to perform complex operations on your data. Operations that are performed in this zone don’t impact other tuples in your dataset. A tuple is a data structure that has more than one part.
- Auditing: With the increasing complexity of the ETL process, the auditing process may get lengthy and complicated. Staged data makes the auditing process easier by allowing you to compare the original input files and the output data files. Staging methods keep the data lineage intact while enabling a smooth auditing procedure.
- Database Functions: The staging area provides the necessary environment and, often, the necessary tools for performing transformations such as replication, aggregation, and cleansing. You can ensure that corrupt data will not be introduced into your business’ main data center.
- Flexibility: If the source or destination systems have hardware or network limitations, a staging area offers flexibility. It can be tailored to adapt to business and data processing cycles.
Data Staging Best Practices
To maximize the effectiveness of data staging, here are some best practices worth adopting:
- Design for Scalability- Design your data staging area to handle future data volumes. Choose platforms and tools that can scale easily without significant architectural changes. Additionally, anticipate data growth patterns and adapt the infrastructure accordingly. A proactive design approach helps save future resource allocations and ensures seamless data integration regardless of volume variations.
- Maintain Data Integrity- It is essential to validate and cleanse the data as it enters the data staging area. This involves implementing checksums, data type validation, and other checks to maintain consistency. Regular reviews and updation of validation protocols can accommodate changing data structures and business needs. By prioritizing data integrity, you can safeguard the quality of your data while also bolstering trust in the insights and analytics derived from it.
- Implement Version Control- A version control system for your data staging area processes ensures you can track changes, revert when required, and understand the data handling history. It also improves team collaboration, allowing multiple contributors to work in parallel without overriding each other’s modifications. Preserving historical versions equips you to handle audits and reviews as well as swiftly address any data anomalies that may arise from changes.
- Prioritize Security- Robust security measures such as encryption, access controls, and audit trails help protect the raw, sensitive data in the staging area. It’s equally important to frequently review and update these measures for emerging threats and vulnerabilities. The staging area must not be accessible to users; only ETL operations should be able to read or write to the staging area’s data files. Additionally, only the ETL team in charge of the entire process must own the data staging space; no one else should have access to it. There are no Service-Level Agreements for staging area files since they’re intended only for display, without any indexes or aggregations to aid queries.
- Maintain Comprehensive Documentation- Ensure every process and data flow within the data staging area is well-documented. Comprehensive documentation serves as a reference for future modifications, ensuring traceability and consistency. It also aids in onboarding and troubleshooting while providing clarity on data lineage and transformations.
- Frequent Backups- Regularly back up the configurations, scripts, and crucial data of the staging area. This facilitates quicker recovery in case of failures and safeguards against data loss. Consistent backup routines also ensure no critical information is overlooked, preserving the continuity and integrity of data workflows. With a disciplined backup schedule, you can instill confidence in your data processes, ensuring operations remain resilient against unforeseen disruptions.
Whether you want to enhance data quality or ensure seamless integration of different data sources, data staging is a critical step. It helps transform raw data into valuable information for effective decision-making.
The various steps involved in data staging include extracting data from multiple sources and transforming and cleansing the data before or after loading to staging areas. Data staging areas can be external or internal and vary based on the organization’s strategy. To be able to reap the maximum benefits of data staging, some best practices include designing for scalability, maintaining data integrity, implementing version control, and prioritizing security.