- September 21, 2020
- Posted by: Apoorva Team
- Category: Development Phase
What are the differences between data warehousing and cloud computing? What cloud-based data warehousing products exist?
Data warehousing and cloud computing are two incomparable concepts. Hence, one cannot conclude as to how these are different from one another. Cloud computing involves storing, managing, and processing data, using a network of remote servers hosted on the internet. Data warehousing is primarily used to integrate various data and “store” it for the purpose of analysis. In a way, data warehousing is a part of cloud computing.
There are various cloud-based data warehouse products available in the market. Some of the prominent and widely used ones are Amazon Redshift and Google BigQuery. Let us have a brief look at these cloud data warehouses:
In recent years, AWS cloud consulting services have garnered popularity among businesses that have set up their cloud infrastructure. Redshift along with other Amazon web services has earned a great reputation and has outdone the traditional data warehouse.
Some of the features that make Redshift better than a traditional data warehouse are:
- Easy setup: Amazon Redshift is a cloud-based data warehouse that can be set up in minutes. Once the details are configured, you are a click away from the setting up of Redshift. Amazon Redshift’s cloud data services will be deployed and factors like monitoring and scaling will automatically be enabled.
- Scalability: You can scale Redshift simply by resizing its cluster size. Thus, to scale up, you must increase the number of compute nodes in the cluster, and to scale down, you must decrease the number of compute nodes in the cluster. It’s as simple as that.
- Better Performance: Amazon Redshift performs almost ten times better than a traditional data warehouse. There are two reasons to be cited for this:
- Use of Columnar data storage: Columnar data can hold three times more data than an average traditional data warehouse. Also, data compression is much easier when compared to row storage.
- Massive Parallel Processing: In the Redshift’s architecture, when the cluster gets an input, the leader node accepts the query. The compute nodes and the node slices work parallelly to solve the query. This automatically boosts the speed of the performance as a lot of processing is done parallelly, and in turn, no time is wasted.
- Cost-Effective: With Redshift, you will not incur any upfront costs. Also, the cost is almost one-tenth of that of a traditional data warehouse.
- Allowing the query of data from data lake: Normally, when data is extracted from a data lake, the process involves a lot of effort (in terms of ETL) and cost. But Amazon Redshift has a feature known as “Amazon Spectrum” that acts as an interface between Redshift and the data lake. Hence you can directly query data from the data lake using the Amazon Spectrum.
- Data security: Your data is safe and secure with Amazon Redshift’s cloud data services. You are offered two features that ensure the security of your data:
- Backup & Recovery: When your data is stored in Amazon Redshift, a copy of it is made and its snapshot is sent to Amazon S3. So in case, you lose your data, you can recover it from the Amazon S3 service.
- Encryption: By enabling the “Encrypt” option in Redshift, the data in the clusters and nodes are secured by encryption.
Google BigQuery is a fully managed data warehouse that runs queries rocket fast. It analyzes terabytes of data in seconds and petabytes of data in minutes. The Google BigQuery data warehousing tool encrypts, replicates, and deploys your data across multiple data centers. It seamlessly integrates with the Google cloud platform, and other software, so you can readily load, process, and make interactive visualizations of your data. Never before was analyzing massive data sets and obtaining final insights was this easy.
Now that we know what BigQuery is, let us look into the standout features that make it the most preferred cloud data warehousing service for tremendously large amounts of data:
- The sharing and collaboration of innumerable data are very easy.
- With BigQuery, you can decide who can access your data, thus ensuring data security.
- Since standard SQL queries are used, anyone among your staff can get involved.
- You can process billions of rows of data in seconds. This is important for real-time analysis of streaming data such as online gaming systems, IoT sensors, etc.
- The Google cloud BigQuery data transfer service securely transfers your data from On-premises to the cloud.
- BigQuery eliminates the hassle of building data warehouse on your own, which can be expensive, time-consuming and difficult to scale.
- It is easy to implement, as all you have to do is just load your data, and pay for what you use.