Data Warehouse Buyer’s Guide
320 business hours!
That’s how long a junior to mid-level data engineer typically spends on researching, understanding requirements, shortlisting, getting a quote, comparing & evaluating, procuring, and implementing a data warehouse. Simply put, a whopping two months. But here’s the scoop: it can be done much quicker if done right.
Choosing the right data warehouse isn’t just about having a place for your data; it's about having the right place. With countless options, the selection process can be challenging. Each tool has its strengths and weaknesses, making it crucial to find one that aligns with your business needs.
You can find the right warehouse in three ways, two of which are arduous:
Browse the web, read reviews, and test the best-rated (often manipulated) tools, which may take months and bring uncertainty about the best fit.
Seek help from communities like Reddit, only to get unspecific answers, which doesn't help the cause.
Use this guide as the single source of truth with insights & tips from our in-house data experts.
First things first, what is a data warehouse?
I came across this 👇 question on Stack Overflow while researching data warehouses:
I was asked by a customer what the term "data warehouse" really means.
I thought about ETL, details of the data model, differences to NoSQL, Clouds, 'normal' DBMS, MDM (Master Data Management) etc. but wasn't able to describe the term in a few words to him... (In fact I did some talking and left him un-illuminated.)
How can "data warehouse" described in 1-3 (or a bit more) sentences?
Keeping it stupidly simple, a data warehouse is the central hub for all the data an organization stores in its databases, raw files, CRM, and other apps. Over time, it becomes a unified repository of valuable historical data. That’s why it’s often referred to as the “single source of truth.”
Every data warehouse undergoes a routine extract, transform, load (ETL) process to gather data, clean it, and store it centrally. This data is then used for reporting, analysis, and mining by data analysts and BI managers.
Now you may wonder how it differs from a data lake or a data mart. I’ll go deeper on Data Lake vs Data Warehouse vs Data Mart in a separate blog real soon.
The 3-step data warehousing process
Now that we've got the lowdown on what a data warehouse is, let's jump into how it works and where it slots into the data architecture.
Data sourcing
Data is collected from various sources, including Online Transaction Processing (OLTP), Operational Data Store (ODS), another data warehouse, or raw data files.
Data warehousing
Data usage
The data from the consumption layer is utilized for reporting, analytics, and business Intelligence (BI).
Here’s a visual representation of the data flow from sources to the consumption layer.
Benefits of using a data warehouse
While there are tons of benefits of using a data warehouse, here are the top three:
Single Source of Truth
A data warehouse consolidates and organizes large amounts of data into a central database for teams to run their analytics on.
Quick decision-making
As data is readily available, a data warehouse makes it easy for stakeholders to access data, speeding up decision-making.
Enhanced data security
In light of growing cyber threats, businesses need to focus on keeping their data safe. Safeguarding data in a central warehouse is relatively straightforward as opposed to several data banks and servers. Most data warehouse solutions provide security features like user groups to control access, slave read-only access, and encryption techniques.
The "slave read-only" access means making a copy of the database that allows only reading, not writing. It helps balance data access, backups, and security by limiting changes to the main database.
Types of data warehouses
Data warehouses are typically grouped under many buckets based on the processing type (real-time vs. batch), storage format (columnar vs. row-based), data structure (relational vs. NOSQL), and more.
However, when selecting a warehouse for your business, you should first evaluate whether you need an on-prem or a cloud-based solution. Here’s what each means:
On-premises data warehouses
On-prem data warehouses, like IBM DB2, Microsoft SQL Server, Vertica, and SAP HANA, are set up in-house. This means the organization handles both the hardware and software.
Ownership and control
With on-premises setup, organizations have total control over everything—hardware, software, and access. Whether rolling with standard or custom servers, the organization keeps hands-on access to the hardware. And if anything goes wonky, the IT team can dive into every software layer to sort things out.
High security
To amp up security, on-prem warehouses rely on encryption, access control lists, and physical security measures. This ensures that sensitive data stays protected, thwarting any unauthorized access, disclosure, or modification.
High availability
On-prem warehouses have backup plans and extra hardware to keep things smooth. If there's a hiccup—like hardware issues, network glitches, or maintenance—the data warehouse easily switches to backups, so the data flow is not interrupted.
High upfront cost
Setting up an on-prem data warehouse costs a pretty penny compared to renting from the cloud. You're dishing out for hardware, software licenses, infrastructure, and skilled personnel. And as tech moves forward, get ready to open your wallet for upgrades, making it a pricier long-term deal.
Maintenance
On-prem data warehouses need regular upkeep to ensure performance and data quality, removing resources from essential business tasks.
Scalability issues
Growing on-prem data warehouses isn't a walk in the park. Businesses might have to invest on new hardware or software, cranking up the costs. Scaling up needs meticulous setup and data spread-out to keep the system running smooth, making it complex and time-consuming.
Cloud-based warehouses
A cloud-based data warehouse is like a virtual storage space for data, but it's not on your regular office servers. Instead, providers like AWS, GCP, or Azure host it in the cloud.
Pay-as-you-go
Cloud-based solutions offer flexible pricing. You don't need to invest upfront in physical servers. Pay only for what you use and avoid spending on idle resources.
Compatibility
Cloud data warehouses smoothly team up with both on-premise and cloud systems. They handle all sorts of data and play nice with BI and ML tools on top cloud providers like AWS, Azure, and GCP.
Ease of use
Cloud data warehouses are a breeze to use! They've got simple interfaces and handy tools for easy setup, configuration, and upkeep.
Highly scalable
Scale on the fly – adjust storage and computing power whenever you need without breaking the bank on new hardware.
Vendor lock-in
Choosing a cloud data warehouse ties you to that provider. Switching can be tricky due to technical bumps and contract intricacies.
Data latency
Things might take a bit longer as your data is outside the organization's network. Cloud services got your back, though, especially those with multi-region support. They stash your data in the regions you fancy, helping cut down on delays.
Questions to ask yourself when deciding on a data warehouse
What’s my budget and the cost structure of the data warehouse?
How’s the performance of the data warehouse?
Should I go for a vendor that supports massively parallel processing (MPP)?
How do I assess security and compliance?
How do I weigh the benefits of batch processing against real-time processing?
What other data integrations do I need?
What type of data do I need to store?
Future trends in data warehouses
Augmented analytics
In simple language, augmented analytics means making data analysis a breeze for everyone by automating insights, improving data exploration, and offering user-friendly interfaces.
In tech speak - Augmented analytics in data warehouses amps up data analysis with machine learning and natural language processing.
Here's what we expect in this space:
- More automation in data preparation tasks
- Smarter query optimization
- Better NLP to reach more skill levels
- Putting the spotlight on human-friendly design for easy-to-use tools
- Tighter teamwork with AI for predicting, spotting anomalies, and digging into advanced insights
Serverless data warehousing
Serverless data warehousing simplifies data management without the burden of server upkeep, promoting flexibility and efficiency in analytics.
Serverless data warehousing is getting slicker, more adaptable, and full of innovation. With continuous strides in cloud tech, serverless architectures, and analytics, this field's set to keep booming. Here's the lowdown:
- Expect to see serverless data pipelines, handling data from start to finish without the fuss of managing infrastructure.
- Look out for clever auto-scaling algorithms predicting and adjusting resources based on workload changes.
- Watch for the rise of multi-cloud orchestration—it lets organizations smoothly use serverless data warehousing across different cloud solutions, ensuring seamless data management.
AI-assisted ETL
As AI tools become more sophisticated, AI-assisted ETL is gaining popularity. It helps automate routine tasks and lowers the risk of human errors.
AI in data warehousing is gearing up to make things more automated, handling real-time data and throwing in some predictive analytics. Here’s my two ounces:
- AI-boosted self-service ETL will make data integration a breeze for teams. More solutions are already on it.
- As AI keeps getting smarter, it looks like we can expect smooth, context-aware transformations in different data environments—like databases, cloud services, on-prem systems, and the like.
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Context-aware transformations in AI-assisted ETL mean the system intelligently adapts data processing based on specific characteristics, business rules, user preferences, real-time conditions, data variability, and historical trends.
How 5X can help you procure and implement a data warehouse
Assessing needs for best-fit vendor recommendations
Choosing the perfect data warehouse vendor can be tricky. Businesses often struggle to find the right tool that perfectly suits their needs.
We get it – it's a maze. Once we dig into your business, checking out your needs, data sources, and security requirements, we'll point you to a tool that fits your budget like a glove.
Creating proof of concepts with your actual data
Building proof of concepts for selected tools is usually a big resource drain, taking 2-3 months and a ton of data team effort.
Don't worry, we make things faster without any hiccups. We create proof of concepts in under two weeks using your actual data. Rigorous testing ensures the chosen vendors not only meet but exceed expectations in key areas like query speeds, data processing, and scalability.
Ensuring best practice
Creating functional data pipelines is no walk in the park for businesses. Setting up the intricate web while sticking to best practices can be quite the challenge.
That's where we come in. 5X Services, our consultancy, can lend a hand in setting up your data pipelines, following the best practices, and running checks to guarantee top-notch data quality and integrity.
Streamlined negotiations and contract handling
Integrating data warehouse vendors with tools is often a headache, taking up precious time and distracting from crucial analytics efforts.
With 1-click onboarding on the 5X platform, all that manual work and upkeep vanishes, so you can put analytics first. The new warehouse vendor easily syncs up with data modeling and ingestion vendors via APIs.
Seamless integration with the rest of your data stack
Integrating data warehouse vendors with tools is often a headache, taking up precious time and distracting from crucial analytics efforts.
With 1-click onboarding on the 5X platform, all that manual work and upkeep vanishes, so you can put analytics first. The new warehouse vendor easily syncs up with data modeling and ingestion vendors via APIs.
Unified billing, user management, and insights
Managing invoices from multiple vendors can be a headache, throwing your finances off balance and adding extra tasks for data teams, stealing time from insights.
On 5X, your data warehouse vendors are on a single monthly bill, streamlining finances. Manage users, monitor usage, and centralize data hassle-free. Let your data team focus on insights rather than infrastructure.
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