With the advent of modern-day cloud infrastructure, many business-critical applications like databases, ERPs, and Marketing applications have all moved to the cloud. With this, most of the business-critical data now resides in the cloud. Now that all the business data resides on the cloud, companies need a data warehouse that can seamlessly store the data from all the different cloud-based applications. This is where Cloud Data Warehouse comes into the picture.
This post aims to help you understand what a cloud data warehouse is, its evolution, and its need. Here are the key things that this post covers:
Table of Contents
What is a Cloud Data Warehouse?
A data warehouse is a repository of the current and historical information that has been collected. The data warehouse is an information system that forms the core of an organization’s business intelligence infrastructure. It is a Relational Database Management System (RDBMS) that allows for SQL-like queries to be run on the information it contains.
Unlike a database, a data warehouse is optimized to run analytical queries on large data sets. A database is more often used as a transaction processing system. You can read more about the need for a data warehouse here.
A Cloud Data Warehouse is a database that is delivered as a managed service in the public cloud and is optimized for analytics, scale, and usability. Cloud-based data warehouses allow businesses to focus on running their businesses rather than managing a server room, and they enable business intelligence teams to deliver faster and better insights due to improved access, scalability, and performance.
Key features of Cloud Data Warehouse
Some of the key features of a Data Warehouse in the Cloud are as follows:
- Massive Parallel Processing (MPP): MPP architectures are used in cloud-based data warehouses that support big data projects to provide high-performance queries on large data volumes. MPP architectures are made up of multiple servers that run in parallel to distribute processing and input/output (I/O) loads.
- Columnar data stores: MPP data warehouses are typically columnar stores, which are the most adaptable and cost-effective for analytics. Columnar databases store and process data in columns rather than rows, allowing aggregate queries, which are commonly used for reporting, to run much faster.
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Hevo supports 150+ data sources and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo loads the data onto the desired Data Warehouse, enriches the data, and transforms it into an analysis-ready form without writing a single line of code.
Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well.
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- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
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What are the capabilities of the Cloud Data Warehouse?
For all the Cloud based Data Warehouse services, the cloud vendor or data warehouse provider provides the following “out-of-the-box” capabilities.
- Data storage and management: data is stored in a file system hosted in the cloud (i.e. S3).
- Automatic Upgrades: There is no such thing as a “version” or a software upgrade.
- Capacity management: You can easily expand (or contract) your data footprint.
Traditional Data Warehouse vs. Cloud Data Warehouse
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Traditional Data Warehouse is also an on-premise Data Warehouse that is located or installed at the company’s office. Companies need to purchase hardware such as servers by themselves. The installation requires human resources and much time.
The organization requires a separate staff to manage and update the Traditional Data Warehouse. Scaling the Warehouse takes time as new hardware needs to be shipped to the destination and then installation.
Cloud Data Warehouse, as the name suggests is the Data Warehouse solution available on the cloud. Companies don’t have to own hardware and maintain it. All the updates, maintenance, and scalability of hardware are managed by 3rd party Cloud Data Warehouse Service providers such as Google BigQuery, Snowflake, etc.
Because of the availability of data on the cloud, companies can easily integrate Cloud Data Warehouses with other SaaS (Software as a Service) platforms and tools for Business Analytics.
What are the Benefits of a Cloud Data Warehouse?
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Previously, if an organization needed data warehousing capabilities then that would require, firstly, either building and configuring an on-site server or renting servers off-site and, secondly, configuring the connections between relevant assets.
Either option requires a significant capital outlay. Cloud-based data warehouses minimize these issues. Cloud-based Data Warehousing services are offered at varying price points that are a fraction of what the previous options would cost in terms of capital, time, and stress.
Apart from ease of implementation, cloud-based data warehouse solutions also offered scalability. Previous iterations would require building capacity that took possible future growth into consideration.
With cloud-based data warehouses, that question is now redundant as your package can be easily scaled to your needs, no matter how they fluctuate over time (as long as it’s within the service’s limits).
What are the Top 5 Cloud Data Warehouse Services?
There are many cloud data warehouse solutions. According to IT Central Station, the top 5 cloud data warehouse providers are:
What are the Challenges of a Cloud Data Warehouse?
Security is a concern for cloud-based data warehousing. This is specifically due to the fact that service providers have access to their customer’s data. While service agreements and public legislation around data privacy do exist, it must be borne in mind that it is possible that these entities could, accidentally or deliberately, alter or delete the data.
Another major security concern is the penetration of cloud systems by hackers who are constantly searching for and exploiting vulnerabilities in these systems in order to gain access to users’ personal data and data belonging to large corporations.
Providers take maximum precautions in protecting users’ data. To this end, users are also offered choices in how their data is stored, such as having it encrypted in order to prevent unauthorized access.
Given the large variety of applications, businesses use today, loading all this data present in different formats into a data warehouse is a huge task for engineers. However, fully-managed data integration platforms like Hevo Data (Features and 14-day free trial) help easily mitigate this problem by providing an easy, point-and-click platform to load data to the warehouse.
How to Choose the Right Cloud Data Warehouse
Making the right choice necessitates a deeper understanding of how these data warehouses operate based on features such as:
- Architecture: elasticity, support for technology, isolation, and security
- Scalability: scale efficiency, elastic scale, query, and user concurrency.
- Performance: Query, indexing, data type, and storage optimization
- Use Cases: Reporting, dashboards, ad hoc, operations, and customer-facing analytics
- Cost: Administration, vendor pricing, infrastructure resources
You should also evaluate each cloud data warehouse in terms of the use cases it must support. Here are a few examples:
- Reporting by analysts against historical data.
- Analyst-created dashboards based on historical or real-time data.
- Ad hoc Analytics within dashboards or other tools for interactive analysis on the fly.
- High-performance analytics for very large or complex queries involving massive data sets.
- Using semi-structured or unstructured data for Big Data Analytics.
- Data processing is performed as part of a data pipeline in order to deliver data downstream.
- Leveraging the concept of Machine Learning to train models against data in data lakes or warehouses.
- Much larger groups of employees require operational analytics to help them make better, faster decisions on their own.
- Customer-facing analytics are delivered to customers as (paid) service-service analytics.
Cloud Data Warehouse Automation – What you Need to Know
To accelerate the availability of analytics-ready data, some modern data integration platforms automate the entire data warehouse lifecycle. A model-driven approach will also assist your data engineers in designing, deploying, managing, and cataloging purpose-built cloud data warehouses more quickly than traditional solutions.
The 3 key productivity drivers of an agile data warehouse are as follows:
- Ingestion and updating of data in real-time: A simple and universal solution for continuously and in real-time ingesting your enterprise data into popular cloud-based data warehouses.
- Workflow automation: A model-driven approach to constantly improving data warehouse operations.
- Trusted, enterprise-ready data: To securely share your data marts, use a smart, enterprise-scale data catalog.
FAQ about Cloud Data Warehouse
1) What is the Data Warehouse lifecycle?
The Data Warehouse lifecycle encompasses all phases of developing and operating a data warehouse, including:
- Discovery: Understanding business requirements and the data sources required to meet those requirements.
- Design: Designing and testing the data warehouse model iteratively
- Development: Writing or generating the schema and code required to build and load the data warehouse.
- Deployment: Putting the data warehouse into production so that business analysts can access the information.
- Operation: Monitoring and managing the data warehouse’s operations and performance.
- Enhancement: Changes are made to support changing business and technology needs.
2) What is Data Warehouse automation?
Historically, data warehouses were designed, developed, deployed, operated, and revised manually by teams of developers. The average data warehouse project, from requirements gathering to product availability, could take years to complete, with a high risk of failure.
Data warehouse automation makes use of metadata, data warehousing methodologies, pattern detection, and other technologies to provide developers with templates and wizards that auto-generate designs and coding that was previously done by hand. Automation automates the data warehouse lifecycle’s repetitive, time-consuming, and manual design, development, deployment, and operational tasks. IT teams can deliver and manage more data warehouse projects than ever before, much faster, with less project risk, and at a lower cost by automating up to 80% of the lifecycle.
Conclusion
This article provided a comprehensive guide on a Cloud Data Warehouse. It also explained the benefits and needs of a Cloud Data Warehouse in detail. It also lists the top Cloud Data Warehouse Services in the market today.
With the complexity involves in Manual Integration, businesses are leaning more towards Automated and Continuous Integration. This is not only hassle-free but also easy to operate and does not require any technical proficiency. In such a case, Hevo Data is the right choice for you! It will help simplify your Data Analysis seamlessly.
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Vernon is enthusiastic about data science and loves to write on diverse topics related to data, software architecture, and integration.