Undoubtedly, Cloud is the future. Businesses of all sizes, industries, and geographies are transforming into cloud-based solutions. Cloud adoption has drastically improved and it has become the easiest way to run any business. Data Warehousing is no exception when it comes to cloud adoption. There are multiple benefits that attract organizations to migrate from an on-premise to a cloud-based data warehousing solution.
Installation & Maintenance
Hardware procurement, operating system installation, applying patches, database deployment are things of the past. The platform is readily available and with minimal IT support and less impact on productivity, the systems can be deployed and made operational.
Easy Licensing Model
Subscription-based licensing & pay-for-what-you-use are some of the licensing options. The initial capital required is much lesser and the Return on Investment is quicker than ever.
Scaling up or down is not just easier but is also cheaper and faster.
Availability & Security
Highly encrypted data storage, asynchronous data back up and disaster recovery has never been this easier. Very limited downtime ensures high degrees of availability.
These are some of the pressing issues in any legacy on-premise base data warehousing solution. These never seemed like issues until cloud-based solutions started making way into the market, and the shortcomings of on-premise solutions are clearly evident. It makes even more sense for organizations to explore and migrate to cloud-based data warehouses. There are several players in the cloud-based data warehousing stream, a few of them are:
Snowflake is a cloud-based software-as-a-service (SAAS) that provides cloud-based data storage and analytic services. Snowflake can be hosted on cloud platforms such as Amazon Web Service or Microsoft Azure. One of the highlights of Snowflake is that it can handle maximum concurrency on reading by isolating the computing resources from storage resources. It ensures greater elasticity and maximum performance with its unique Multi-Cluster shared data architecture, thus adding compute resources during peak load periods and scaling down the same when loads subside. It has unique features like Zero copy clone and Time Travel. Snowflake can handle both structured and semi-structured data like AVRO, JSON, etc. The storage resources can be scaled independent of the computing resources and hence data loading and unloading can be done without worrying about running queries and workloads. In Snowflake, the compute and storage are billed separately; storage charges are based on terabyte compressed per month and compute charges are billed on a pay-per-second model. Snowflake provides security by encrypting all the data that is stored on disks.
2. Google’s BigQuery
BigQuery by Google is a serverless, highly scalable, cost-effective cloud data warehouse with an in-memory BI Engine and with built-in machine learning capabilities. It is powered by the Google Cloud Platform, which is a one-stop-shop for all Google Cloud Products. BigQuery can source data from various cloud storage-based source systems. As per the requirements, either you can bring the data into BigQuery or you can access the external data remotely through a powerful federated query option without data replication. BigQuery’s high-speed Streaming Insertion API provides support for real-time analytics with BI and AI capabilities. By having separate storage and computation layers, you can choose the best-fit storage and processing units for your business. Also, you have an option to choose between on-demand and flat-rate pricing models. It can integrate very well with various leading BI Data Reporting/Visualization tools like Tableau, Qlik, Looker, SAP Analytics Cloud, etc.
3. Amazon’s Redshift
Amazon Redshift is a Data Warehousing offering available as part of Amazon web services (AWS). It offers enterprise-level relational database system with columnar data storage structure that can store petabytes of data. With its massively parallel processing and read optimization techniques it can provide analytics with the best performance. Modeling between structured and semi-structured data file systems is possible in Redshift. Its powerful analytics tool called Amazon Quick Sight is loaded with Machine Learning capabilities that give you diagnostic analytics and best predictions. Amazon Quick Sight is not just limited to Amazon products but with ODBC and JDBC drivers you can connect it to different reporting tools like Tableau, Spotfire, etc. It works on a pay-for-what-you-use model and you can easily scale up/scale down the storage based on the requirements. You can even readily load live streaming data with Amazon Kinesis Data Firehose and get real-time analytics from it.
4. SAP Data Warehouse Cloud
SAP, of late, is also getting into the race and it has revealed its own enterprise-level solution called SAP Data Warehouse Cloud. It is powered by SAP HANA Cloud Services which helps to integrate all the heterogeneous data in one place. You can seamlessly integrate on-premise as well as cloud data sources. Fueled by SAP HANA in-memory technology, it is expected to give unmatched performance benefits. Additionally, it is expected to provide real-time business benefits with out-of-the-box advanced analytics options using SAP Analytics Cloud. It has a concept called “Spaces” that helps to build a logical area for each line of business and lets you manage the storage and computation for each space. You can leverage predictive, planning and machine learning capabilities that are in-built into the solution. It is also loaded with prebuilt templates and business content that are ready to be consumed. It helps users to get instant benefits and businesses will run better on SAP Data Warehouse Cloud.
The very first look of the solution on videos and blogs has created a real buzz in the SAP installed base. The product is under active development and it is expected for a beta release soon. Register for the beta program here.
For more information and insights on SAP Data Warehouse Cloud click here.