Business Intelligence Cloud Data Warehouses: A Comprehensive Guide By Ritinder Kaur Business Intelligence No comments Last Reviewed: November 18, 2024 Cloud data warehouses are the next best thing to happen to information storage after massively parallel processing (MPP) databases. Considering the cloud component, how will they complement your existing business intelligence systems and tech stack? What will it entail, and do you have the budget? In this article, we discuss their evolution from relational databases and the challenges, some best practices and considerations to keep in mind before choosing one for your organization. Compare BI Software Leaders Key Points What Is a Cloud Data Warehouse? Do You Need It? Benefits History Things To Consider Cloud Migration Challenges Best Practices What Is a Cloud Data Warehouse? A cloud warehouse is a type of information storage software available in the public cloud with built-in scalability and ease of use to support analytics and reporting. Similar to on-premise data warehouses, it stores and manages your user and performance metrics. It can include customer relationship management (CRM), point-of-sale (POS), supply chain (SCM), inventory, human capital management (HCM) and procurement information. Storage warehouses are computing powerhouses with MPP at the backend. Columnar storage supports reporting through fast aggregate queries. They aren’t mere repositories. Instead, they support big data integration, transformation, organization, reporting and analytics. Do You Need It? You know you need a cloud storage solution if: It’s an enormous task getting your information to work together, with much of it in siloed repositories. Your business performance is subpar, with poor quality data informing critical decisions. Your existing systems are overwhelmed with large volumes of complex, disparate information. Storage warehouses give you: Complete control over your information ecosystem. Dedicated speed and performance. Information governance with regulatory compliance. But, conventional warehouses aren’t great at scaling and lag when overwhelmed with huge datasets. Traditional warehouses hit capacity early, slowing down with increasing storage and computing demands. Large volumes, fast information streams and growing business needs overwhelm these legacy systems. Tight computing-storage coupling leaves no scope for flexible scaling. Conventional warehouses are infrastructure-heavy with rigid architectures. Maintenance is resource-intensive, and downtime can severely impact your bottom line. They weigh you down with hardware, licensing and systems engineering overheads. Achieving business agility with fluctuating workloads is an uphill task. They aren’t flexible enough to support live queries alongside batch and streaming updates. Did you know that on-premise warehouses offer more storage and better processing power for a one-time price? Why use cloud warehouses, then? Compare Top Big Data Analytics Software Leaders Primary Benefits These warehouses combine the advantages of storage systems with the perks of the cloud — fast computing, cost-effective storage options and high availability. Cloud data warehouses form low-latency connections to other cloud-based systems and on-premise platforms. Additionally, they auto-scale with workloads through efficient resource allocation. Managed services keep you worry-free by providing scalability, reliability, security and exceptional performance. Interconnected cloud services reduce implementation efforts further. Ready-to-go warehouse infrastructure and pre-designed provisioning templates lessen the work involved in setting up storage solutions. 24/7 availability ensures business continuity, and backup and recovery options during upgrades give you peace of mind. Failover and redundancy are baked into service-level agreements (SLAs), and downtime is minimal with round-the-clock remediation support. You can avail the benefits of machine learning (ML) and artificial intelligence (AI). Much of the new augmented tech exists in the cloud out of the box. Receive Advice From the Experts History The 1980s was the age of the relational database, with data tables and standard query language (SQL) functions. It was only a matter of time before big data proved too overwhelming for such systems, and MPP databases — Teradata, Netezza, Vertica and Greenplum — were launched. The launch of Hadoop, an open-source computing solution, was an industry milestone. With a master/slave architecture, Hadoop performs distributed data processing on-premises and in the cloud. Its architecture consists of a cluster of computers efficient at simultaneous storage and computing. Hadoop’s strength lies in managing big data with great agility at a low cost. After relational and MPP databases, cloud warehouses are the next significant milestone in storage solutions. It would help to know some standard terms for our discussion ahead. Serverless architecture: It’s a development-focused concept that involves using a set of managed services to provide for all the parts of an application. For instance, developers might call on a service to handle incoming requests with a computing app, a managed datastore and a messaging service for managing queues, pub/sub tasks and more. Auto-scaling: It’s a technique for allocating computing resources with real-time workloads. Multi-cloud: It’s a computing model where an organization uses two or more clouds, whether public, private or a combination of the two, to provide services. Redshift was an early disruptor in big data storage systems. It’s a fully managed service based on PostgreSQL, with an architecture similar to on-premise warehouses. Deployment options vary from cluster-based to serverless, and auto-scaling balances performance with fluctuating workloads. With a pay-as-you-go model, Redshift integrates with enterprise data warehouses (EDWs), data lakes and operational data stores (ODS). Google BigQuery is another serverless warehouse. The solution integrates with Apache Hadoop, Spark and Beam. Machine learning models with automated recommendations help you perform segmentation in seconds. Microsoft Azure Synapse Analytics is an integrated solution consisting of Spark, Azure Data Lake Storage (ADLS) and Azure Data Factory. You can manage storage and prepare digital assets for ML tasks, all in the cloud. Vertical and horizontal scalability gives it the flexibility to handle large, complex datasets. Snowflake is a leading multi-cloud solution that addresses scalability and performance issues caused by high query volumes. The platform combines warehousing with the flexibility of big data platforms and cloud-native elasticity. Besides app development, Snowflake supports secure information exchange, augmented lakes and advanced analytics. Compare Top Big Data Analytics Software Leaders Things To Consider Deployment models and pricing are two significant considerations when seeking to implement a cloud warehouse. Deployment Model Both cluster-based and serverless versions are available for leading warehouse solutions like Redshift. Which one will fit you? Managing clusters is a full-time responsibility — you’ll need to adjust the number of nodes to accommodate fluctuating task loads and monitor their health consistently. Serverless is a modern concept, Google BigQuery being a case in point. It does away with clusters and queries automatically scale up to a preset throttle point. Managed services free you of handling capacity or tracking system performance. Computing Costs Though storage charges are minimal, the computing cost varies. Some vendors charge for the amount of information processed or the computing time used. It sounds ideal, but budgeting can be a pain since it’s tricky to predict usage — you can’t anticipate the number of queries or their size. With cluster-based deployments, pricing is by node, which makes it easier to estimate the overhead before you go all in. But you end up paying a flat rate, irrespective of idle time. Having your stakeholders weigh in on these key decisions gives you the direction to proceed with the software selection process. Cloud Migration Challenges Migration to the cloud is trending, and how. This industry-wide shift is due to cost-effective storage and computing, scalability and a lean architecture. IndustryARC predicts the cloud warehouse market will reach $39.1 billion by 2026. However, migrating to the cloud isn’t without its challenges. Downstream systems are at risk of disruption. Business analysts and power users must adapt and re-orient their reports and dashboards. Asking very early on, “Is performance better now that we are on the cloud?” doesn’t help. You might feel the system didn’t match expectations, especially since on-premise warehouses have dedicated connections to business systems with no latency issues. Unanticipated operating costs can be like a kick to the gut. What can you do? Asking the right questions of vendors can go a long way toward ensuring a seamless migration. What will the process entail? How will they ensure the secure migration of digital assets? How can you minimize migration risks? How much will it cost? How soon might you see improved performance post migration? Get our Big Data Requirements Template Best Practices How can you ensure the migration is a success? Much of what you can do involves the human element — orienting the company mindset to become more data-centric, focusing on maintaining information quality and sanctity. 1. Engage All Stakeholders Involve all the departments in the process. Considering it only as the IT department’s responsibility is a mistake. Migration fails aren’t always due to hardware or software issues — the human element is critical. Driving stakeholder participation is key to a successful implementation. Ensure every department is on board with the deployment process and all necessary resources are available. 2. Know Your Business Gain and share an understanding of your existing IT environment, your migration goals, the eventual benefits to your organization and the best approach to the process. A cost-benefit analysis ahead of deployment can help preempt unanticipated costs. 3. Evaluate Your Digital Assets Data profiling helps move away from guesswork by gauging the time and effort required to ensure the information is fit for priority tasks. It’s a great way to identify essential requirements versus the nice-to-have ones and keep quality in your crosshairs as you prepare to migrate. Get started with our requirements template. 4. Establish a Governance Culture It’s essential to establish a data governance culture across the organization. Devise a plan by asking the right questions. Which resources will be involved? What are “good data” and “bad data?” What should you do to address a data quality exception? Who should you escalate to if unable to make a decision? How will you address issues that arise? The issue queue might be longer in the initial phase after deployment, which will likely plateau after achieving a steady state. How will you ensure data quality after the warehouse solution is live? 5. Choose a Best-fit Service Provider Select the right service provider with the necessary migration tools. Manage expectations on both sides by having SLAs in place. Selecting a migration partner just because they charge less or are known to you isn’t ideal. A less-than-satisfactory migration will likely need more work, derailing your budget. Compare BI Software Leaders Wrapping up It remains to be seen when technologies like the data mesh will become affordable enough to disrupt the market significantly. A data mesh connects datasets mutually without the need to store them in one repository. Does it mean the end of information stores as we know them? What do you think? Which new technologies will be game-changers in the cloud data warehouse market? Let us know in the comments below. Ritinder KaurCloud Data Warehouses: A Comprehensive Guide06.05.2024