A guide to cloud data warehouse

Modern day businesses require faster access to consumable data for decision making, actioning upon real-time insights and driving successful analytics initiatives. As the expectations for on-demand data exploded, data warehousing teams increasingly realized the need to upscale the traditional warehouses, thus shifting to cloud storage. Cloud data migration and warehousing has become critical for businesses owing to its scalability and cost-effectiveness. There is an evident sign of more businesses investing in the cloud data warehouses, such that the market is expected to grow by USD 10.42 billion in value by 2026.

Empowering business users with real-time decision support systems relies on a high-performance and adaptable data infrastructure which seamlessly integrates with multiple data sources. Cloud data warehouses (CDW) offer a host of benefits including higher elasticity, enhanced governance, better resilience, and end-to-end data management at much lower operating costs than on-premise infrastructure.

Adopting a cloud migration strategy and transitioning to a cloud data warehouse calls for a methodical, well-planned approach that mitigates implementation risks and enables businesses to derive greater value and achieve higher ROI from data. The whitepaper discusses how companies can embark on a cloud-first data strategy to store and manage data in the cloud.

Today businesses are harnessing large streams of data, faster than ever before. Thanks to the proliferation of advanced data analytics and business intelligence tools, companies now have the means to quantify the impact of every campaign they run, identify process improvement opportunities, nurture customer engagement across their purchase journey, drive loyalty and ensure exceptional customer experience.

The abundance of data pipelines consisting of performance, customer, competitive and process-oriented data has made it easier for companies to drive actionable decision making. However, this data boom also poses a serious challenge for companies. With time the size of their data sets can quickly outgrow the capability of their native systems to store and process the data.

Traditionally data warehouses served as a preferred architecture for companies to collate, organize and access enterprise data hosted on servers on-premise. But, finite storage capacity, cost of ownership, higher latency and the need for constant maintenance didn’t make conventional data warehouse solutions a sustainable data storage option. Hence, the next logical data storage innovation happened in the cloud.

With time as business expectations for on-demand data exploded, data warehousing teams increasingly shifted their data storage efforts to the cloud. The need to seamlessly harvest data from diverse sources and presenting it in an easily consumable format to decision-makers has made adopting cloud migration strategy indispensable today.

For the cloud data migration process to be successful, companies need to ensure that they have the right data integration and data management capabilities.

While both data lakes and data warehouses are commonly used to store large amounts of data, the words are not synonymous. A data lake is a large pool of unstructured data which is not well defined. On the other hand, a data warehouse is a repository of filtered data that has already been processed for a particular purpose. The table below illustrates some of the key differences to keep in mind.

A well-thought out cloud data warehouse strategy can help companies reap multiple benefits such as:

Seamless access to the data repository

Cloud data warehouses offer seamless remote access from anywhere which means that administrators can troubleshoot from home or anywhere outside of their work hours if required. Remote access capabilities also provide companies with the opportunity to hire staff across locations, thereby opening up talent pools that were previously unexplored. Cloud data warehousing provides self-service options and thus for its provisioning, companies do not need to rely on specialized employees.

Low operating costs

Purchasing and maintaining an on-premise data center can be a costly affair. Companies need to ensure that the area housing a data center is cooled and that there is proper insurance coverage and availability of expert staff who can help with maintenance and troubleshooting 24X7. Cloud data warehousing, on the contrary, allows a company to achieve the same level of data management competency by only paying for the storage and computing power required. Today, the availability of flexible cloud data services such as Snowflake provides users with the liberty to purchase compute and storage separately depending on the requirements.

Enhanced performance

Cloud data service providers mainly differentiate on the grounds of providing the most efficient compute and storage power for a fraction of the cost that would be incurred to replicate the same power on-premises. In the cloud data warehousing model, all upgrades are routinely performed automatically, so companies can have the latest capabilities at their disposal without experiencing downtimes while upgrading to the latest versions.

Increased scalability

Opting for a cloud data services subscription can be as simple as opening an account with big providers like AWS Redshift, Microsoft Azure, Snowflake, and Google BigQuery. The subscription is often customizable and companies can easily scale their storage and computing capabilities based on requirements.

Unmatched availability and security

While on-premise data warehouses historically have high uptime, they are not completely immune to costly outages, downtimes, and failures. Additionally, human errors and phishing attempts can also render an on-premise data warehouse vulnerable, making service unavailability and security a major pain point for companies. All major CDW companies focus on reliability features, such as data replication across regions and data centers to ensure constant service uptime and security.

There are certain aspects which companies must consider while transitioning from a traditional data warehouse to a cloud data warehouse.

Creation of a clear business case

While deciding on a cloud migration strategy, companies need to determine what exact value they are looking to derive from the transition – is it cost savings, enhanced data capacity, greater agility, or faster query performance? A clear business case can help determine a seamless and successful migration strategy.

Evaluation of the existing data warehouse

Companies should implement a robust enterprise data cataloging solution to get a clear picture of the data stored in the on-premise data warehouse and determine the data governance and data lineage across systems.

Deciding a robust cloud migration strategy

There are three key options when it comes to migration strategies – lift and move on-premise warehouse to the cloud “as-is”, incrementally shift on-premise data warehouse to cloud, or develop a new cloud data warehouse from scratch. In order to derive the quickest value, companies should consider transitioning incrementally to the cloud while focusing on key use cases.

Selecting the right platform and data management environment

Before moving to the cloud, a company needs to decide whether it wants to manage the infrastructure in-house (IaaS) or let the cloud service provider do so (PaaS). Cloud data management services and tools that encompass both on-premise and cloud can help businesses minimize disruption while shifting data incrementally between on-premises and cloud environments.

Systematic migration and operationalization

In order to derive the best results, companies need to clearly define the test and acceptance standards at the initial migration phase. There should also be an adequate focus on planning the testing cycles and executing the migration process to shift ETL, Schema, metadata, data, applications, and users.

The transition to cloud data warehousing necessitates a methodical, well-planned approach along with some data management practices.

 

Need for a well-planned data strategy

Redundancies and inaccuracies are all typical issues when data is migrated from one system to another. When data migration occurs, advanced systems may view some data as a flaw, whereas older systems may perceive it as a useful data point. Some of the most common challenges and strategies to solve these are:

ETL (extract, transform, load) is a critical procedure in the entire cloud data migration strategy through which companies collate data from diverse sources and load it in the centralized cloud data warehouse. During the ELT/ETL process, relevant data is extracted from source files such as spreadsheets or databases and then processed and transformed to comply with the data standards of the cloud warehouse. This transformed data is finally loaded in the data warehouse.

ELT/ETL for data warehouse and analytics is an indispensable component. However, the best ELT/ETL tools vary on the basis of use cases and unique business requirements. Here is an indicative list of some of the latest ELT/ETL tools that companies can consider:

 

Worldwide, spending by businesses on cloud computing and storage infrastructure is forecast to top $1 trillion in 2024. Going forward, cloud storage or cloud data warehouse is clearly going to play a critical role in enterprise cloud transformation strategy. While the move to cloud sure looks like a safe bet, business intelligence teams must keep a few important things in mind before jumping on the bandwagon, such as:

As a process, data modeling requires the business intelligence team to closely evaluate data process and storage. Data modeling techniques ideally include methods that govern data representation patterns, the preparation of data models, and communication of business requirements. The techniques essentially depend on some formalized workflows that include a sequence of objectives to be performed in an iterative manner, such as:

In cloud data warehouse architecture optimizing data retrieval speeds for generating analytics is the key. And this is where dimensional data models help create a difference. While Entity-relationship (ER) data models and relational data models emphasized efficient data storage practices, dimensional models focus on enhancing redundancy in order to make it easier for business intelligence teams to easily locate relevant data for retrieval and reporting purposes.

The two most popular dimensional data models include the star schema and the snowflake schema. In a star schema, data is cataloged as dimensions (reference information) and facts (measurable items). Here each fact is mapped with its related dimensions in a pattern that represents a star. The snowflake schema is similar to the star schema but it involves multiple layers of associated dimensions that make the entire pattern quite complex.

What are the benefits of star schema?

Star schema model offers the following benefits:

What are the benefits of snowflake schema?

Snowflake schemas deliver the following benefits:

Data transmission from multiple disparate sources to a cloud data warehouse is done through an extract-transform-load or an extract-load-transform workflow. Selecting the right workflow is extremely critical in cloud data warehouse design. In the ETL workflow, data transformation is done before loading considering that no further transformation is required for analyzing and reporting. ETL served as the preferred workflow until the proliferation of cloud data warehouses with better data processing capabilities. Cloud data warehouses do not need to completely transform data before loading and data can be transformed eventually on the basis of requirements.

This data warehousing model offers the following benefits:

While cloud data warehousing allows companies to collect and store large amounts of raw, structured and unstructured data, the majority only manage to process a small portion of the data they collate. Given this, cloud deployments that combine compute and storage often end up investing in additional compute capacity which in most cases stay underutilized.

While mass storage has become cheaper with time, computation has remained expensive. By separating storage and compute, data teams will have the opportunity to scale storage capacity economically depending on the data load while also scaling distributed computing depending on the data processing needs.

Business intelligence teams need to implement stringent data governance measures while building cloud data warehouses in order to ensure data availability, integrity, security and usability. A meticulous data governance strategy can help data teams ensure that data is trustworthy and reliable across channels and is not mistreated. Over the years, efficient data governance has evolved as a critical requirement in enterprise cloud migration initiatives, given the need for organizations to comply with stringent data control and privacy regulations.

As a part of the data governance process, creating a strong data lineage can help BI teams better understand, document and visualize data from its origin to the consumption stage. Data lineage can also help in identifying errors, implementing quick changes within the data management process and carrying out systems migration to save time. Other best practices that can help BI teams reinforce their data lineage process are data cataloging and data archival. While the former helps data engineers capture data asset metadata through a data mapping framework, the latter helps in storing data through indexes and metadata, irrespective of how the data may have originally been stored during active use. Data archival also helps companies reduce the cost to a significant extent by shifting cold or infrequently used data in lower-tier storage systems such as AWS Glacier, Azure Archive Storage and so on.

The majority of organizations today are investing in cloud storage solutions building a cloud data warehouse or data lake on Azure, AWS, Databricks or Snowflakes. However, not all of them stand at a similar point in the overall organizational maturity curve. Whether a company is attempting a cloud shift for the first time or whether it is looking to simply modernize the data storage infrastructure in the cloud, the journey can raise several questions.

Similar to forming a team or developing a process, a company may need to closely understand how well its cloud migration efforts are progressing. It needs to religiously adhere to the best practices, follow the established standards and procedures, review performance against those standards and implement corrective measures if there are deviations. A meticulously developed maturity model from cloud experts can help a company understand where it stands in terms of implementation and the measures needed in order to improve.