Data warehouses have become essential for organizations dealing with massive amounts of data, and while other alternatives come to market their adoption remains on the rise. This increased adoption accounts for a projected Compound Annual Growth Rate (CAGR) of 10.7% from 2020 to over $51B in 2028. The growth rate comes as no surprise, as there is increased demand for a dedicated storage system to handle business analytics at scale with low latency. Show
Organizations must design a warehouse architecture that fits their data needs while maintaining standard best practices to harness the full potential of data warehouses. Businesses may choose to adopt a cloud data warehouse, a service provided by cloud providers, or host their data warehouses on-premise. Each approach has its control, scalability, and maintenance trade-offs. Data warehouses usually consist of data warehouse databases; Extract, transform, load (ETL) tools; metadata, and data warehouse access tools. These components may exist as one layer, as seen in a single-tiered architecture, or separated into various layers, as seen in two-tiered and three-tiered architecture. Let’s explore data warehouse architecture and best practices when designing a data warehouse solution. The Components of Data Warehouse ArchitectureData warehouses consist of four essential components:
Data warehouse design can be complex, as the data warehouse must possess the ability to integrate several data sources and store massive volumes of data while operating at low latency and high performance. Applying layers to warehouse architecture plays a huge role in improving performance and data consistency. There are three main data warehouse architecture types;
Best Practices for Data Warehouse ArchitectureData warehouses house massive volumes of data, leading to high latency and low performance if engineers fail to follow the best practices. Here are some best practices to maintain when designing data warehouses:
Principles of Data Warehouse ArchitectureData warehouses should fulfill the following properties in their architecture design:
Data Marts: Customized Mini Data WarehousesMost layered data warehouse architecture contains a specialized, virtualized view of data that caters to a particular group of users within an organization.. This more focused, data subset refers to data marts. For instance, the finance department may decide to want to perform predictive analysis for some of its customers. In this case, engineers may use a specific subset of consumer data from the data warehouse to create a data mart that best serves this model purpose. In this case, it eliminates unnecessary data, which hastens business intelligence and analysis. Data marts are essential due to the following reasons:
Architecture differences between cloud data warehouses and on-premise data warehousesToday, the cloud is often part of modern data warehouse architecture. Let’s look at some of the differences between on-premises and cloud data warehouses. Cost and Ease of AdoptionTraditional data warehouses can be capital and time intensive due to the costs of acquiring, setting up hardware components, and human resources to operate these hardware components. For cloud data warehouses, companies can easily set up their data warehouses by leveraging data warehouse solutions provided by cloud providers like AWS and Google Cloud at a non trivial fraction of the cost. Maintenance CostsWith complete control comes total responsibility. For on-premises/traditional data warehouses, the perks of maintaining full control mean that the burden of ensuring high performance, efficient operations, and reducing downtime rests solely on their shoulders. Hence, this control creates additional work for engineers. For cloud-hosted data warehouse solutions, cloud service providers have a set Service Level Objective (SLO) for uptime, and ensuring availability and maintenance is a shared responsibility. Speed and PerformanceTraditional data warehouses are said to possess faster performance as the presence of dedicated hardware eliminates the restrictions on throughput and I/O on the disk side. However, because most disk capacity grows faster than the I/O throughput rates provided by these disks, more disks are needed to serve an acceptable performance for an increased number of users. Also, because it’s challenging to estimate the I/O bandwidth for a DW before building, a sudden increase in users requiring more I/O bandwidth for fast performance becomes an issue for on-premises DW, as acquiring more storage capacity is expensive and time-consuming. On the other hand, for cloud data warehouses, scaling up storage to improve performance takes minutes. Also, most cloud providers offer multi-location redundancy that helps speed up network access in multiple locations to serve a broad audience. Scalability and flexibilityCloud data warehouses can scale up and down in minutes according to fluctuations in volumes according to the demand of their applications. However, this fast scaling isn’t the case for traditional data warehouses, as users are forced to manage to a series of limitations imposed by the hardware. Ease of IntegrationsCloud data warehouse design easily supports the automatic integration of new data sources like databases, social media, and other cloud applications. On the other hand, combining various data sources in your traditional data warehouse can be tedious and requires massive setup and hardware, which is a time-intensive venture. For cloud data warehouses, data integration becomes seamless by using the over 100 StreamSets connections. Additionally, most modern data warehouse cloud solutions come with in-built monitoring and optimization tools to help monitor the performance and health of your data warehouses. However, engineers face the burden of performing integrations and monitoring for traditional data warehouse solutions. Disaster RecoveryOn-premises data centers require backup centers to house duplicate data in case of disasters. These backup centers introduce additional hardware acquisition, setup, and maintenance costs. In most cases, backup centers may never be used and have no protection against the occurrence of natural phenomena like natural disasters. Most cloud data warehouses are inherently redundant in design and support duplication of data, snapshots, and backups in disaster cases. Alternatives to Data Warehouses: Data Warehouses vs. Data Lakes vs. Data Lakehouses.Other solutions for storing massive amounts of data include data lakes and lakehouses. Unlike data warehouses with the primary purpose of storing large amounts of processed data for performing business analytics and business intelligence, the end use cases for storing in data lakes is flexible access to RAW data. Instead, data lakes help keep massive volumes of data without a predefined structure. Data lakes store massive amounts of data (structured, unstructured, and semi-structured). At the same time, data warehouses store vast amounts of processed and filtered data for use in analytics. A data lakehouse combines the best features of data warehouses and data lakes to provide a more robust and scalable storage solution. Data lakehouses combine the massive scale and cost-efficiency of data lakes with the ACID transactions of data warehouses to perform business intelligence and analytics on data. An example of a data lakehouse is the Databricks Lakehouse. Conclusion:StreamSets helps drive quick and tangible value to your data warehouse by providing 100s of pre-built source connections and an easy to operate GUI-based design interface. This opens up the value to large groups of stakeholders with varying skillsets. With automated jobs, you can automate StreamSets pipelines to deliver data to your data warehouse and sources that support change data capture. Multi-tale updates mean you don’t have to wrestle with your schema design when updating or migrating a data warehouse. You simply press play on your smart data pipeline and your schema will populate correctly. Last, whether you standardize on a data warehouse, a data cloud, or a data lakehouse, StreamSets supports these platforms with the same features and a unified developer experience. What is data warehouse means?A data warehouse is a central repository of information that can be analyzed to make more informed decisions. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence.
What are the four stages of data warehousing?7 Steps to Data Warehousing. Step 1: Determine Business Objectives. ... . Step 2: Collect and Analyze Information. ... . Step 3: Identify Core Business Processes. ... . Step 4: Construct a Conceptual Data Model. ... . Step 5: Locate Data Sources and Plan Data Transformations. ... . Step 6: Set Tracking Duration. ... . Step 7: Implement the Plan.. What makes a data warehouse?A data warehouse is a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics. Data warehouses are solely intended to perform queries and analysis and often contain large amounts of historical data.
What is a data warehouse and what are the key attributes of a data warehouse?A data warehouse is built by integrating data from various sources of data such that a mainframe and a relational database. In addition, it must have reliable naming conventions, format and codes. Integration of data warehouse benefits in effective analysis of data.
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