A Comprehensive Guide to Cloud Data Warehousing

Welcome to the only guide you need to better understand what cloud data warehousing is and how it works. This guide will lead you through all of the essential aspects and cover necessary tools and tips that will be of great use if you have to work with data warehouses.

Without further ado, take a look below and try to take in as much as possible. These details will be vital if you plan on using this technology.

What is a Data Warehouse?

A data warehouse is an extensive collection of data to help a business or an organization to make informed decisions.
In a more sophisticated language, a data warehouse is a kind of decision support system. It stores historical data from across the organization and processes it, allowing experts to use the data for critical business analyses, reports, and dashboards.

This kind of system stores data coming from numerous sources. These sources are usually structured; Online Transaction Processing (OLTP) data such as invoices and financial transactions, Enterprise Resource Planning (ERP) data, and Customer Relationship Management (CRM) data.

The data warehouse places special emphasis on data relevant for business analysis, organizes and optimizes it to enable efficient analysis.

Top Data Warehouse Tools To Consider

It is undeniable that data warehousing boosts access to information, speeds up query-response times, and allows businesses to fetch deeper insights from big data. However, to truly utilize the power of data warehousing (cloud data warehousing included), one has to be familiar with some useful data warehousing tools.

Amazon Redshift

Amazon Redshift is a cloud-based data warehousing tool designed for enterprises. The fully-managed platform can process petabytes of data in a matter of seconds, which is the reason why it’s suitable for high-speed data analytics.

It also supports automatic concurrency scaling — the automation increases or decreases query processing resources to match workload demand. As a result, users can execute hundreds of concurrent queries without the operational overhead.

Google BigQuery

Google’s BigQuery is one of the fiercest rivals of Amazon Redshift. That’s why many compare BigQuery vs RedShift, and you should do the same if you want to choose a tool that is the perfect fit for your business.

BigQuery is a cost-effective data warehousing tool with built-in machine learning characteristics. Users can integrate it with Cloud ML and TensorFlow to create powerful AI models. Also, it can execute queries on petabytes of data in seconds to give real-time analytics.

The tool is a cloud-native data warehouse that supports geospatial analytics. Users can analyze location-based data or discover new lines of business with it.
On top of that, this tool can separate compute and storage. It enables users to scale processing and memory resources based on business needs. The separation allows users to manage the availability, scalability, and cost of each resource.

Snowflake

Snowflake is usually used to set up an enterprise-grade cloud data warehouse. With the tool, users can analyze data, including various unstructured and structured sources.

The multi-cluster, shared architecture allows the storage to be separated from processing power. That is how it allows users to scale CPU resources based on their activities.

What is a Cloud Data Warehouse?

A cloud data warehouse is a database located in a cloud as a managed service optimized for analytics, scale, and ease of use.

So, the only difference between a traditional data warehouse and this one is that everything is located online (in the cloud).

Cloud data warehouse benefits

Data warehouses that are cloud-based allow companies to focus on running their business rather than running a room full of servers. Also, they enable business intelligence teams to deliver faster insights thanks to improved access, scalability, and performance.

  1. Scalability. It is faster and less expensive to scale a cloud data warehouse when compared to an on-premise system. It does not require users to buy new hardware, and the scaling can happen automatically whenever needed.
  2. Data access. Placing the cloud data allows companies to give their analysts access to real-time data from numerous sources, allowing them to run better analytics faster.
  3. Performance. A cloud-based data warehouse allows for queries to be run much more quickly than against a traditional on-premises data warehouse and at a more affordable price.

Data Warehouse Components

Even though data warehouse architecture has not changed much for more than twenty years, some new trends have recently switched things up a bit.

Traditional enterprise data warehouse architecture

These were usually built relying on a three-tier architecture:

  1. Bottom tier – database server used to extract data from multiple sources.
  2. Middle tier< – OLAP server, which transforms data to enable analysis and complex queries
  3. Top tier – these tools are used by businesses for high-level data analysis, reporting, querying, and data mining.

Developers usually structured data warehouses using either virtual data warehouse, data mart, or enterprise data warehouse.

A virtual data warehouse is practically a set of separate databases. These databases can be queried together, forming one virtual data warehouse.

Datamart is a small data warehouse set up for business-line-specific reporting and analysis. Together, the data marts comprise the organization’s data warehouse.

Finally, an enterprise data warehouse (EDW) is a large data warehouse holding aggregated data that spans the entire organization.

Cloud Data Warehouse Architecture Example

We will take Amazon’s Redshift as an example. This tool is characterized by the process where the tool computing resources are provisioned in clusters.

These have one or more nodes, where each node has its own CPU, storage, and RAM. A leader node compiles all of the queries and transfers them to compute nodes, which execute them.

When it comes to each node, data is stored in chunks that are called slices. Amazon Redshift uses columnar storage, meaning each block of data has values from a single column across several rows, contrary to a single row with values from multiple columns.

Final Words

Hopefully, you now better understand how data warehouses function and why they exist. Cloud data warehouses, basically, are similar to the traditional ones. However, since they are stored in the cloud, they naturally have various advantages over other data warehouses.

If you need to work on these warehouses or already work with data warehousing, go through the guide again to ensure you got everything right. Understanding this technology is the first step to a successful endeavor with data warehousing.

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