Businesses today increasingly rely on data analytics to provide insights, identify opportunities, make important decisions, and innovate. Every day, a large amount of data (a.k.a Big Data) is generated from multiple internal and external sources that can and should be used by businesses to make informed decisions, understand their customers better, make predictions, and stay ahead of their competition.
For effective data-driven decisions, securely storing all this data in a central repository is essential. Two of the most popular storage repositories for big data today are data lakes and data warehouses. While both store your data, they each have different uses that are important to distinguish before choosing which of the two works best for you.
What is a Data Lake?
With large amounts of data being created by companies on a day-to-day basis, it may be difficult to determine which method will be most effective based on business needs and who will be using the data. To visualize the difference, each storage repository functions similarly to how it sounds. A data lake, for example, is a vast pool of raw, unstructured data. One piece of information in a data lake is like a small raindrop in Lake Michigan.
All the data in a data lake is loaded from source systems and none is turned away, filtered, or transformed until there is a need for it. Typically, data lakes are used by data scientists to transform data as needed. Data warehouses, on the other hand, have more organization and structure – like a physical warehouse building. These repositories house structured, filtered data that is used for a specific purpose. Still both repositories have many more layers to them than these analogies suggest.
A data warehouse is the traditional, proven repository for storing data. Data warehouses use an ETL (Extract, Transform, Load) process, compared to data lakes, which use an ELT (Extract, Load, Transform) process. The data is filtered, processed, and loaded from multiple sources into the data warehouse once its use is defined. This structure, in turn, allows for its users to run queries in the SQL environment and get quick results. The users of data warehouses tend to be business professionals because once the data is fully processed, there is a highly structured and simplified data model designed for data analysis and reporting.
While the structure and organization provided by a data warehouse is appealing, one major downside you might hear about data warehouses is the time-consuming nature of changing them. Since the use of the data in data warehouses is already identified, the complexity of changing the data loading process for quick reporting takes developers a lengthy amount of time. When businesses want fast insights for decision-making, this can be a frustrating challenge if changes to the data warehouse need to be made.
In terms of cost, data warehouses tend to be more expensive in comparison to data lakes, especially if the volume of data is large. This is because of the accessibility, which is costly to make changes to. However, since a data warehouse weeds out data outside the identified profile, a significant amount of space is conserved reducing overall storage cost.
What are the Benefits of a Data Warehouse?
While data lakes come with their own benefits, data warehouses have been used for decades compared to data lakes, proving their strong reliability and performance over time. Thus, there are several benefits that can be derived using a strong data warehouse, including the following:
Saves Time: With all your data loaded and stored into one place, a lot of time is saved from manually retrieving data from multiple sources. Additionally, since the data is already transformed, business professionals can query the data themselves rather than relying on an IT person to do it for them.
Strong Data Quality: Since a data warehouse consists only of data that is transformed, this refined quality removes data that is duplicated or inadequately recorded.
Improves Business Intelligence: As data within a data warehouse is extracted from multiple sources, everyone on your team will have a holistic understanding of your data to make informed decisions.
Provides Historical Data: A data warehouse stores historical data that can be utilized by your team to make future predictions and thus, more informed decisions.
Security: Data warehouses improve security by allowing certain security characteristics to be implemented into the setup of the warehouse.
Should I use a Data Lake or a Data Warehouse?
Due to their differences, there is not an objectively better repository when it comes to data lakes and data warehouses. However, a company might prefer one based on their resources and to fulfill their specific business needs. In some cases, businesses may transition from a data lake to a data warehouse for different reasons and vice versa. For example, a company may want a data lake to temporarily store all their data while their data warehouse is being built. In another case, such as our experience with McDonald’s France, a company may want a data lake for an ongoing data collection from a wide ranges of data sources to be used and analyzed later. The following are some of the key differences between data lakes and data warehouses that may be important in determining the best storage repository for you:
User type: When comparing the two, one of the biggest differences comes down to who is using the data. Data lakes are typically used by data scientists who transform the data when needed, whereas data warehouses are used by business professionals who need quick reports.
ELT vs ETL: Another major difference between the two is the ELT process of data lakes vs ETL process of data warehouses. Data lakes retain all data, while data warehouses create a highly structured data model that filters out the data that doesn’t match this model. Therefore, if your company wants to save all data for later use – even data that may never be used – then a data lake would be the choice for you.
Data type: As the more traditional repository, the data in data warehouses consists of data extracted from transaction systems with quantitative metrics and attributes to describe them. On the other hand, data lakes embrace non-traditional data types (e.g. web server logs, social network activity, etc.) and transforms it when it is ready to be used.
Adaptability: While a well-constructed data warehouse is highly effective, they do take a long time to change if changes need to be made. Thus, a lot of time can be spent getting the desired structure. Since data lakes store raw data, the data is more accessible when needed and a variety of schemas can be easily applied and discarded until there is one that has some reusability.
Furthermore, different industries may lean towards one or the other based on industry needs. Here’s a quick breakdown of what a few industries are using most commonly to store their data:
Education: A popular choice for storing data among education institutions are data lakes. This industry benefits from the flexibility provided by data lakes, as student grades, attendance, and other data points can be stored and transformed when needed. The flexibility also allows universities to streamline billing, improve fundraising, tailor to the student experience, and facilitate research.
Financial Services: Finance companies may tend to choose a data warehouse because they can provide quick, relevant insights for reporting. Additionally, the whole company can easily access the data warehouse due to their existing structure, rather than limiting access to data scientists.
Healthcare: In the healthcare industry, businesses have plenty of unstructured data including physicians notes, clinical data, client records, and any interaction a consumer has with the brand online. Due to these large amounts of unstructured data, the healthcare industry can benefit from a flexible storage option where this data can be safely stored and later transformed.
There is no black-and-white answer to which repository is better. A company may decide that using both is better, as a data lake can hold a vast amount of structured and unstructured data working alongside a well-established data warehouse for instant reporting. The accessibility of a good warehouse will be available to the business professionals of your organization, while data scientists use the data lake to provide more in-depth analyses.
Choosing between a data lake and data warehouse, or both, is important to how your data is stored, used, and who it is used by. If you are interested in setting up a data lake or data warehouse and want advisory on your next steps, 2nd Watch has a highly experienced and qualified team of experts to get your data to where it needs to be. Contact us to talk to one of our experts and take your next steps in your cloud journey.
If the global pandemic taught us anything, it’s that digital transformation isa must-have for businesses to keep up with customer demands and remain competitive. To do this, organizations are moving their workloads to and modernizing their applications for the cloud faster than ever.
In fact, according to a recent survey, 91% of respondents agree or strongly agree that application modernization plays a critical role in their organization’s adaptability to rapidly changing business conditions. But there are so many cloud service providers to choose from! How do you know which one is best for your application modernization objectives? Keep reading to find out!
What is a Cloud Services Provider (CSP)?
A cloud services provider is a cloud computing company that provides public clouds, managed private clouds, or on-demand cloud infrastructures, platforms, and services. Many CSPs are available worldwide, including Alibaba Cloud, Amazon Web Services (AWS), Google Cloud Platform (GCP), IBM Cloud, Oracle Cloud, and Microsoft Azure. However, three industry giants are noteworthy because of their services and global footprint: Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.
What is Application Modernization?
Application modernization is the process of revamping an application to take advantage of breakthrough technical innovations to improve the overall efficiency of the application remarkably. This efficiency typically involves high availability, increased fault tolerance, high scalability, improved security, eliminating a single point of failure, disaster recovery, contemporary and simplified tools, new coding language, and reduced resource requirements, among other benefits. Many companies running legacy applications are now looking at how they can best modernize their monolith applications.
Application Rationalization: The First Step to Modernization
The best way to start any application modernization journey is with application rationalization. In this process, you identify company-wide business applications and strategically determine which ones you should keep, replace, retire, or consolidate. Once you identify those applications, you can list each one’s ease or difficulty level, total cost of ownership (TCO), and business value, enabling you to decide and prioritize which action to take. (Hint: Start with high value and minimal effort apps!) Doing this will also help you eliminate redundancies, lower costs, and maximize efficiency.
The high-value apps that are difficult to move to the cloud will likely cause the most grief in your decision-making process. But, like Rome, your modernization strategy doesn’t need to be built in a day.
You can develop an approach to application modernization over time and still reduce costs and risks while moving your portfolio forward.
It is crucial to evaluate your current application stack and determine the most suitable application modernization strategy to migrate to the cloud when it comes to application modernization in the cloud. Many on-premises applications are legacy monoliths that may benefit more from refactoring than a rehosting (“lift and shift”) approach. (Check out Rehost, Refactor, Replatform – What, When, & Why? | AppMod Essentials)
Refactoring may require overhauling your application code, which takes some high-level effort but offers the most benefits. However, not all applications are ideal candidates for refactoring. Rearchitecting will become necessary for some obsolete applications that are not compatible with the cloud due to architectural designs made while building the app. In this scenario, the value proposition considers rearchitecting, dividing the application into several functional components that can be individually adapted and further developed. These small, independent pieces—or “microservices”—can then be migrated to the cloud quickly and efficiently.
Determining the Best Cloud Services Provider for Your Application Modernization
Each application modernization journey is unique, as is the process of choosing the best cloud service provider that meets your demands. What works for one business’ application may not be the best for yours, even if they are in the same industry. And just because a competitor has chosen one CSP over another does not mean you should.
When evaluating the CSP that is best for you, consider the following:
Service Level Agreements (SLAs): Determine if the CSP’s service level agreements suit your production workloads, whether the cloud service is generally available yet, and they retain satisfactory levels of support knowledge. Managing workloads in the cloud can sometimes be tedious. The managed services department may not have the required expertise to efficiently manage and monitor the cloud environment. It is critical to your business to do your due diligence to ensure your preferred CSP can administer their managed offerings with as close to zero downtime as possible.
Vendor Lock-in: It is important to have alternatives to any single CSP and that you retain the flexibility to substitute for a better value proposition.
Enterprise Adoption: Consider the likelihood of scalability of your use of the CSP across your organization.
Economic Impact: Consider the positive business or financial impacts that result from the service usage at the individual, department, and company-wide levels.
Automation and Deployment: Verify the CSP’s integration capabilities with your organization’s preferred automation tooling and availability of automated and local testing frameworks.
When modernizing existing applications to take the best advantage of the cloud, cloud technologies like serverless and containers are good options to consider. Serverless computing and containers are cloud-native tools that automate code deployment into isolated environments. Developers can build highly scalable applications with fewer resources within a short time. They both also reduce overhead for cloud-hosted web applications but differ in many ways. Private cloud, hybrid cloud, and multi-cloud approaches to application modernization are worth considering too.
Serverless Computing and Containers
Serverless computing is an exaction model where the CSP executes a piece of code by dynamically allocating the resources and can only charge for the services used to run the code. Code is typically run in stateless containers. Various events such as HTTP requests, monitoring alerts, database events, queuing services, file uploads, scheduled events (cron jobs), and more can trigger them.
The cloud service provider then receives the code in a function to execute, which is why serverless computing is sometimes referred to as a Function-as-a-Service (FaaS) platform. Add that to your list of as-a-Service acronyms: IaaS, PaaS, SaaS, FaaS!
Containersprovidea discrete environment set up within an operating system. They can run one or more applications, typically assigned only those resources necessary for the application to function correctly. Because containers are smaller and faster than virtual machines, they allow applications to run quickly and reliably among various computing environments. Container images become containers at runtime and include everything needed to run an application: code, runtime, system tools, system libraries, and settings.
Private, Hybrid, and Multi-Cloud
The public cloud is a vital part of any modernization strategy. However, some organizations may not be ready to go directly to the public cloud from the datacenter. Cloud architects should consider private, hybrid, and multi-cloud strategies in those cases. These models can help resolve any architectural, security, or latency concerns. They will also reduce the complexity associated with the policies for specific workloads based on their unique characteristics.
Migration to the cloud is ideal for investing in application modernization as it can lower your overall operational costs and increase your application’s resiliency. But not all use cases—nor cloud service providers—are the same. You need to do your homework before choosing the best-suited one for your business.
2nd Watch offers a comprehensive consulting methodology and proven tools to accelerate your cloud-native and app modernization objectives. Our modernization process begins with a complete assessment of your existing application portfolio to identify which you should keep, replace, retire, or consolidate. We then develop and implement a modernization strategy that best meets your business needs.
From application rationalization to application modernization and beyond, 2nd Watch is your go-to trusted advisor throughout your entire modernization journey.
Contact us to schedule a brief meeting with our specialists to discuss your current modernization objectives.