Exploring Cloud Based Data Warehousing: Key Insights

So, you’ve come across cloud-based data warehouses and would like to figure out how they operate?

You’re in the right place. We will reveal how these cloud-based data warehouses work behind the scenes.

Picture this: a virtual haven where huge amounts of information are methodically organized, analyzed, and stored with unmatched efficiency. The cloud-based data warehouse has transformed data management in a virtual setting by centralizing storage and processing. It offers unmatched scalability and flexibility.

Understanding how cloud-based data warehouses work is not only crucial for technology enthusiasts. It’s also vital for businesses striving to stay competitive in an increasingly data-driven landscape.

Why?

Cloud-based data warehouses have changed the way organizations handle their data. They empower organizations to make informed decisions, drive innovation, and gain a competitive edge in their respective industries.

So, let us demystify the cloud-based data warehouse. This way, you will appreciate how elegant and efficient these cloud marvels are.

Table of Contents:

1. What is a Cloud-Based Data Warehouse?
2. What is a Data Warehouse used for?
3. How Does Cloud-Based Data Warehouse Work?
4. Traditional vs. Cloud-Based Data Warehouse
5. Cloud Data Warehouse Examples
6. Key Features of Cloud-Based Data Warehouse Solutions
7. Most Popular Cloud Base Data Warehouse
8. How Do We Implement Data Warehousing in the Power BI?
9. How To Perform Data Warehousing By Using Power BI?
10. Cloud Data Warehousing Challenges
11. Key Benefits of a Cloud-Based Data Warehouse
12. How to Choose a Cloud-Based Data Warehouse Solution?
13. FAQs Cloud Data Warehouse
14. Wrap Up

First…

What is a Cloud-Based Data Warehouse?

Definition: A cloud-based data warehouse is a virtual repository for storing, managing, and analyzing data in the cloud. This data warehouse operates digitally, unlike traditional data warehouses, which rely on on-premises hardware. It leverages cloud computing resources to store massive volumes of data, offering scalability and flexibility.

Data is stored in a structured manner and organized into tables and columns for efficient querying and analysis. You can access the data warehouse remotely through the internet, eliminating the need for physical infrastructure maintenance. Cloud-based data warehouses employ distributed computing techniques to process queries quickly and efficiently, regardless of the dataset size.

This modern approach to data warehousing allows businesses to adapt to changing data needs rapidly and cost-effectively. It makes it a popular choice for organizations seeking to harness the power of big data.

What is a Data Warehouse used for?

A data warehouse is used to centralize and store large amounts of structured data from multiple sources, allowing businesses to perform complex queries, generate reports, and conduct data analysis. It supports decision-making, business intelligence, and trend analysis by providing a unified, consistent view of the organization’s data over time.

How Does Cloud-Based Data Warehouse Work?

Data in cloud-based data warehouses flows like a digital river, meticulously organized and effortlessly analyzed. Let’s delve into the inner workings of these celestial repositories.

1. Data ingestion: It all begins with the influx of data from various sources, ranging from transactional databases to streaming platforms. Cloud-based data warehouses seamlessly ingest this data, ensuring nothing is lost in transit.
2. Data storage: Once inside the warehouse, data finds its home in the cloud’s expansive storage infrastructure. Here, it’s stored in a structured format optimized for efficient retrieval and analysis.
3. Data processing: The cloud flexes its computational muscles as data undergoes processing. Complex algorithms crunch numbers, identify patterns, and cleanse the data, preparing it for analysis.
4. Query execution: When users pose questions to the data, the warehouse springs into action, executing queries with lightning speed. Distributed computing techniques ensure optimal performance, even with vast datasets.
5. Storage management: As data accumulates, efficient storage management becomes paramount. Cloud-based data warehouses automatically handle storage optimization, archiving rarely accessed data while keeping frequently accessed data ready.
6. Security and compliance: In the digital age, safeguarding data is non-negotiable. Cloud data warehousing platforms employ robust security measures, like encryption and access controls, to protect sensitive information. This also helps to ensure compliance with regulations.
7. Integration with analytics tools: Data must interface seamlessly with analytics tools to extract meaningful insights. Cloud-based data warehouses integrate with many analytics platforms, empowering you to unlock the full potential of your data.

Traditional vs. Cloud-Based Data Warehouse

Category	Traditional Data Warehouse	Cloud-Based Data Warehouse
Infrastructure	Requires on-premise hardware	Fully hosted in the cloud
Scalability	Limited, costly to scale	Scalable on-demand
Cost	High upfront and maintenance costs	Pay-as-you-go model
Maintenance	In-house IT support needed	Managed by the cloud provider
Flexibility	Difficult and slow to upgrade	Easily adjustable and flexible
Performance	May degrade as data grows	Optimized with automatic scaling
Accessibility	Location-bound, on-premise access	Accessible from anywhere online

Cloud Data Warehouse Examples

• Amazon Redshift

A fully managed, scalable data warehouse service from AWS that allows you to run complex queries and perform big data analytics.

• Google BigQuery

A serverless, highly scalable, and cost-effective data warehouse from Google Cloud, designed for running super-fast SQL queries on large datasets.

• Microsoft Azure Synapse Analytics

An integrated analytics service from Microsoft that combines big data and data warehousing capabilities, enabling complex queries and data analysis.

• Snowflake

A cloud-native data platform that provides a fully managed data warehouse with capabilities for data storage, processing, and sharing.

• IBM Db2 Warehouse on Cloud

A fully managed data warehouse solution from IBM that offers high-performance analytics and flexible deployment options.

Key Features of Cloud-Based Data Warehouse Solutions

Cloud-based data warehouse solutions offer a host of key features. Each feature empowers you to seek actionable insights and make data-driven decision-making.

Why?

• Scalability: As businesses grow, their data volumes also increase. Cloud data warehouse solutions provide the ability to scale as needed. They handle increased amounts of data without the need for expensive hardware updates.
• Flexibility: Flexibility is crucial in a changing business landscape where agility is essential. Cloud data warehouses offer the versatility to quickly and effectively adjust to changing data needs. This allows your company to remain ahead of the game.
• Cost-efficient: Traditional data warehouses require significant initial investments and continuous expenses for upkeep. Cloud-based solutions provide a pay-as-you-go structure, eliminating the necessity for capital expenses. This enables you to expand your data operations in a cost-efficient manner.
• Accessibility: Thanks to cloud data warehouses, critical business insights are no longer limited to the confines of the office walls. Remote accessibility allows decision-makers to have real-time intelligence by analyzing data at any location and time.
• Innovation: The cloud is a hotbed of innovation, constantly evolving to meet the demands of modern businesses. Cloud data warehouse solutions leverage cutting-edge technologies like machine learning and artificial intelligence. This helps to unlock new insights and drive innovation across industries.

Most Popular Cloud Base Data Warehouse

1. Amazon Redshift

• Widely used for its scalability and integration with the AWS ecosystem.
• Known for its high performance and support for complex queries.

2. Google BigQuery

• Popular for its serverless architecture and ability to handle large-scale data analytics.
• Offers real-time insights with fast query performance.

3. Microsoft Azure Synapse Analytics

• It is favored for its integration of big data and data warehousing capabilities.
• Provides robust analytics tools and seamless integration with other Microsoft services.

4. Snowflake

• Renowned for its cloud-native architecture and ability to handle diverse data workloads.
• Offers features for data storage, processing, and sharing with ease of scalability.

5. IBM Db2 Warehouse on Cloud

• Known for its high-performance analytics and flexible deployment options.
• Provides a fully managed environment with strong support for various data models.

How Do We Implement Data Warehousing in the Power BI?

Power BI opens a Navigator window that lets you select data sources. You start with the connections table in our service schema, which records all client connections to data sources.

Using the Data view in Power BI Desktop’s left column, you prepare the data. Then, surplus columns are removed by right-clicking their names and selecting Delete.

A properly formatted date for sorting and a monthly display for each connection is needed. You add a new column with a formatted version of the created timestamps from the connections table. Using Power BI’s Modeling tab, _New Column is chosen and the formatting is specified using PowerBI’s Data Analytics Expression (DAX) language.

Focusing solely on connections linked to Google Analytics, a text filter on the “type” field is implemented to restrict the query to rows containing the string “google”.

Next, you return to the Navigator and import data from another source: the rjm_clients table from our platform schema, encompassing all client information. Unnecessary columns are eliminated, retaining only the client ID and a Boolean indicator of client activity. Then, proceed to establish how Power BI should link the two tables. Click the Relationships and Manage Relationship buttons in the Home tab. This brings up the Relationships view, where foreign key relationships are configured by associating key fields across tables, specifically the client IDs.

With all desired data filtered and appropriately joined, a transition to the Report view is made.

Here, a new Measure is created using a DAX function, defining “connects” with COUNT(connections[client_id]). In this syntax, connections is the table, and client_id is the field name.

Next, the line chart icon in the visualization pane is clicked. “YM” is dragged to the Axis field, “Type” to the Legend field, and “connects” to the Values field.

Upon initial data review, it was noticed that the timeframe needs to be revised. Filters are applied in the report pane, similar to those in the data pane, to limit only the “type” field to “Google” values.

How To Perform Data Warehousing By Using Power BI?

Here is a step-by-step process for creating a visualization in Power BI.

Stage 1: Logging in to Power BI

• Log in to Power BI.
• Enter your email address and click the “Submit” button.

• You are redirected to your Microsoft account.
• Enter your password and click “Sign in“.

• You can choose whether to stay signed in.

• Once done, the Power BI home screen will open.

Stage 2: Creating a Data Set and Selecting the Data Set to Use in Your Chart

• • Go to the left-side menu and click the “Create” button.
  • Select “Paste or manually enter data“.

• We’ll use the HR sample data below for this example.

Application Channels	Initial Screening	Conduct Interviews	Employee Onboarding	Total Candidates
Social Media	Short Listed	Final Interview	Hired	32
Social Media	Short Listed	Final Interview	Not Hired	400
Social Media	Short Listed	Knocked Out		800
Social Media	Knocked Out			1100
Company Career Page	Short Listed	Final Interview	Hired	20
Company Career Page	Short Listed	Final Interview	Not Hired	250
Company Career Page	Short Listed	Knocked Out		500
Company Career Page	Knocked Out			900
Events	Short Listed	Final Interview	Hired	5
Events	Short Listed	Final Interview	Not Hired	100
Events	Short Listed	Knocked Out		200
Events	Knocked Out			350
Paper Media	Short Listed	Final Interview	Hired	3
Paper Media	Short Listed	Final Interview	Not Hired	80
Paper Media	Short Listed	Knocked Out		135
Paper Media	Knocked Out			700
Employee Referrals	Short Listed	Final Interview	Hired	10
Employee Referrals	Short Listed	Final Interview	Not Hired	70
Employee Referrals	Short Listed	Knocked Out		80
Employee Referrals	Knocked Out			110
Direct	Short Listed	Final Interview	Hired	25
Direct	Short Listed	Final Interview	Not Hired	150
Direct	Short Listed	Knocked Out		425
Direct	Knocked Out			600

• Paste the above data table in the Power Query Window.
• Select the “Create a dataset only” option.

• On the left-side menu, click “Data Hub“.
• Power BI populates the data set list. (If you have not created a data set, refer to the Error! Reference source not found section).

• Click on the “Create a report” dropdown.

Stage 3: Adding the Power BI Sankey Diagram Extension by ChartExpo

• Creating the Sankey Diagram requires us to use an add-in or Power BI visual from AppSource.
• Navigate to the right side of the Power BI dashboard.
• Open the Power BI Visualizations panel.
• Click the ellipsis symbol (…) as highlighted in the diagram below. This will import the Power BI Sankey Diagram extension by ChartExpo.
• The following menu opens:
• Select the “Get more visuals” option.

• The following window opens.
• Enter “Sankey Diagram for Power BI by ChartExpo” in the highlighted search box.
• You should see the “Sankey Diagram for Power BI by ChartExpo”, as shown in the image below.

• Click the Sankey Diagram option and click the “Add” button.

• You can now see the Sankey Diagram for Power BI by ChartExpo icon in the visualizations list.

Stage 4: Drawing a Sankey Diagram with ChartExpo’s Power BI extension

• Select the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.
• The following window opens in the report section of your dashboard:

• Select the fields to use in your Sankey chart here.

• One thing is essential when you select the fields: you must follow the sequence below:
  • Application Channels
  • Initial Screening
  • Conduct Interviews
  • Employee Onboarding
  • Total Candidates
• You’ll be asked for a ChartExpo license key or email address.

• Add the key under the Visual section.

• After adding the key, you can see a chart for your data.
• You can add the top header text in the chart under the General section as follows:

• After that, you can click on Visual set, the number, and enable the options below:

• You can change the “Node Font Style” as follows:

• You can change the “Level Font Style” as follows:

• You can see the Level Labels as follows:

• You can change the Node color by following the steps below:

• Now we show you “How to change Level 1 color,” and then by following this, you can change the node color of other levels as follows:

• Below is the final look of the HR Dashboard in Power BI using ChartExpo after changing the nodes’ color.

Insights

• Social Media is the top application channel, boasting the highest candidate count at 1100.
• The company career page closely follows, with 900 candidates.
• Events and Paper Media show comparatively fewer candidate counts at 350 and 700, respectively.
• Direct applications account for 600 candidates.
• Employee Referrals contribute 110 candidates to the pool.
• Social Media has the highest number of shortlisted candidates (32 Hired, 400 Not Hired).
• Company Career Page and Direct applications also exhibit substantial numbers of shortlisted candidates.
• Paper Media and Events show lower counts of shortlisted candidates.
• Social Media records the highest count of Knocked Out candidates at 800, followed by the Company Career Page at 500.
• Employee Referrals and Direct applications have lower counts of Knocked-out candidates.

Cloud Data Warehousing Challenges

1. Latency and Performance

Cloud-based systems may experience latency due to network bandwidth issues, affecting data processing and query performance.

2. Cost Management

Although cost-efficient, cloud services can become expensive with growing data volumes and increased usage if not monitored properly.

3. Vendor Lock-In

Migrating data between different cloud providers can be challenging, leading to dependency on a single vendor’s infrastructure and tools.

4. Skill Gaps

Organizations may face a shortage of skilled professionals to effectively manage and optimize cloud data warehouses.

5. Data Governance

Managing data ownership, quality, and accessibility in the cloud requires clear policies and governance frameworks.

Key Benefits of a Cloud-Based Data Warehouse

1. Cost Efficiency

Pay-as-you-go pricing reduces upfront infrastructure costs and allows for more efficient spending.

2. Accessibility

Access data from anywhere with an internet connection, enabling real-time collaboration and remote work.

3. High Performance

Cloud data warehouses offer fast query processing and automatic resource allocation to handle large datasets efficiently.

4. Integration with Modern Tools

Seamless integration with advanced analytics, AI, and BI tools enables better insights and data-driven decision-making.

How to Choose a Cloud-Based Data Warehouse Solution?

1. Cost Structure

Evaluate the pricing model (pay-as-you-go vs. subscription) and ensure it fits your budget and expected usage.

2. Performance & Latency

Assess the platform’s query speed, performance benchmarks, and ability to handle large datasets without latency.

3. Data Security & Compliance

Ensure the solution offers robust security features (encryption, access control) and complies with industry regulations (GDPR, HIPAA).

4. Integration Capabilities

Check if the solution integrates well with your existing data sources, ETL tools, and analytics platforms.

5. Ease of Use & Management

Opt for a platform that’s user-friendly and doesn’t require extensive management, with automated maintenance features.

6. Vendor Support & Reliability

Consider the provider’s customer support, uptime guarantees, and service-level agreements (SLAs) for reliability.

FAQs Cloud Data Warehouse

Which cloud data warehouse is best?

Determining the best cloud data warehouse depends on specific needs and preferences. Popular options include Amazon Redshift, Google BigQuery, Snowflake, and Microsoft Azure Synapse Analytics. Each offers unique features and pricing structures tailored to different use cases and organizational requirements.

What are the characteristics of a cloud-based data warehouse?

Cloud-based data warehouses are scalable, flexible, and cost-effective solutions for storing and analyzing large volumes of data. They offer seamless integration with various data sources, robust security measures, high performance, and ease of use.

What is the difference between a database and a data warehouse?

A database is designed for transactional processing, handling real-time data interactions. In contrast, a data warehouse is optimized for analytical processing, storing, and analyzing large volumes of historical data. Data warehouses often consolidate data from multiple sources for comprehensive analysis.

Wrap Up

In conclusion, the workings of cloud-based data warehouses are a testament to modern data management’s evolution. Through scalable infrastructure and flexible architecture, they accommodate diverse data needs with ease. These warehouses process queries swiftly by leveraging distributed computing, unleashing insights from vast datasets.

The journey begins with data ingestion, where information from various sources finds its celestial abode in the cloud. Here, it undergoes meticulous storage and organization, setting the stage for efficient processing. With the power of distributed computing, data is transformed and analyzed, paving the way for actionable insights.

Security measures safeguard sensitive information, while integration with analytics tools empowers you to extract actionable insights. The pay-as-you-go model of cloud data warehouses ensures cost-effectiveness, eliminating the need for hefty upfront investments in hardware.

From data ingestion to query execution, every step in the process is meticulously orchestrated. With a focus on scalability, flexibility, and cost-effectiveness, these warehouses offer a strategic advantage in the data-driven landscape.

In essence, the workings of cloud-based data warehouses epitomize the convergence of technology and necessity. They represent a technological evolution and a paradigm shift in how we approach data management.