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How is CI/CD (Continuous Integration / Continuous Delivery) Used to Modernise a Data Warehouse?
A specially designed type of data management, a data warehouse is a system that has been built to enable and support business intelligence and analytics. For example, a data warehouse will read large amounts of data to understand relationships and trends within an organisation.
Data warehouses contain large amounts of historical data and are intended to perform queries and carry out analysis. The data within a data warehouse is often derived from various sources, such as application log files and transaction details. You’re centralising and consolidating large amounts of data into one location by utilising a data warehouse. Over time, this data can be invaluable to data scientists and business analysts, allowing informed business decisions to be made using valuable business data insights. As this data record builds, a data warehouse is often considered an organisation’s single source of truth.
Analytics and data have become indispensable to allow businesses to stay competitive, and companies rely on reports, dashboards, and analytic tools to extract data, monitor business performance, and support future business decisions. The power behind these processes are data warehouses, which store data efficiently to minimise the input and output of data and deliver query results quickly.
What’s the Architecture of a Data Warehouse?
A data warehouse architecture is created in tiers. The top tier is the front-end, where results are presented through reporting, analysis, and data mining. The middle tier is where the analytics engine used to access and analyse data sits. The bottom tier of the architecture is the database server, where data is loaded and stored.
Data is stored in two types of ways:
- Data accessed regularly is stored in fast storage, like an SSD drive.
- Whilst data that is less frequently accessed is stored in an object store.
The data warehouse is competent at differentiating between the two types of data. It will automatically ensure that frequently accessed data is available in the fast storage option, thereby optimising query speed.
So, how does a Data Warehouse work?
A data warehouse might contain multiple databases, and within each database, the data will be organised into tables and columns. Within each column, you can then define a description of the data, for example, integer, string, or data field. Tables can be organised within schemes, using a similar structure to files and folders. So, data is added to a data warehouse, it’s then collected and stored into various tables defined by the schema, and query tools utilise the schema to identify which data tables to access and analyse.
What are the benefits of using a Data Warehouse?
As we mentioned above, there are multiple benefits to using a data warehouse, for example, making more informed business decisions based on data and insight. Here are some of the additional benefits of using a data warehouse:
- Consolidated data from many sources
- Historical data analysis
- Data quality, consistency, and accuracy
- Separation of analytics processing from transactional databases and improves the performance of both systems
What’s a Modern Data Warehouse?
Across your organisation, multiple teams and users will have different needs for a data warehouse. Traditional data warehousing can’t always keep up with the demands of rapidly growing volumes of data, processing workloads and analysing data. In contrast, a modern data warehouse architecture addresses the different needs of your organisation by providing a way to manage everything you need with various integrated components that all work together.
A modern data warehouse would typically include:
- A streamlined database that simplifies the management of all data types and provides different ways to use data across your organisation
- Self-service data ingestion and transformation services
- Support for SQL, machine learning, graph, and spatial processing
- Multiple analytics options that make it easy to use data without moving it
- Automated management for simple provisioning, scaling, and administration
Ultimately, a modern data warehouse can efficiently streamline data workflows in a way that traditional data warehouses can’t. This allows everyone to perform their jobs more effectively and efficiently, from analysts and data engineers to IT teams and data scientists.
If you have a lot of data, and multiple teams that all need to access it, then modernising your data warehouse should be a key component of your data strategy. But how can you update your data warehouse? This is where CI/CD, which stands for Continuous Integration (CI) and Continuous Delivery (CD), comes into play. CI/CD creates a faster, more precise, and overall efficient way of combining the work of multiple teams into one streamlined product. For example, in app development and operations (DevOps), CI/CD would streamline coding, testing, and deployment by creating a single space for storing work and tools, thereby consistently combining and testing code to ensure it works.
What is the CI/CD pipeline, and how can it support modernising a Data Warehouse?
By utilising a CI/CD pipeline, any software development or engineering processes that combine automated code building with testing and deployment, you can deploy new and updated software safely and precisely.
In other words, a CI/CD pipeline is the behind-the-scenes plumbing of your data and analytics, making your life easier and your work more consistent. Here at Engaging Data, we have developed a solution that integrates WhereScape 3D & RED with CI/CD and DevOps pipelines in one ecosystem to modernise the world data warehousing. If you’d like to know more about our CI/CD pipeline, take a look here.
Because a CI/CD pipeline isn’t just a linear process, it allows DevOps teams to write code, integrate it, test, deliver updates & releases, and make changes to the software in real time. In addition, the ability to automate critical parts of the CI/CD pipeline allows development teams to work more efficiently and more effectively and improve other DevOps metrics.
What are the benefits of the CI/CD pipeline in modernising a Data Warehouse?
The most significant benefit of a CI/CD pipeline is the automation of releases from the initial testing to deployment. Additional benefits of the CI/CD pipeline for DevOps include:
- Automated testing makes the development time more efficient; CD and automation mean that a developer’s changes to a cloud application could go live within minutes.
- Thanks to faster, more efficient testing and development, less time is needed to be spent in the development phase, therefore reducing cost.
- The CI/CD pipeline is a continuous code, test, and deploy cycle. Every time code is tested, developers can react to feedback and improve the code.
- A CI/CD pipeline allows a more collaborative and integrated process with everyone across the organisation who needs to access the data warehouse.
If you have any questions about a CI/CD pipeline or the deployment process, we have diagrams of the flow here.
Conclusion
Overall, a modern data warehouse utilising CI/CD should be an essential part of your data strategy if your organisation has multiple touchpoints requiring access to data, insight, and analytics.
Do you have any questions about modernising your data warehouse? Or about creating and implementing a CI/CD pipeline? Our expert team of data specialists can implement a modern data warehouse for your organisation.
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