Data Engineering Services vs Warehousing vs Analytics: Pick Your Data Strategy
With data becoming a crucial part of the global industry, it is essential to unlock its full potential to boost your business. Here, we’ll discuss data engineering services, data warehousing, and data analytics to help determine the best choice.
Data is the key to a successful business. Instead of storing the data in outdated setups like silos, you can create a central data repository and allow employees restricted access to the datasets. This makes it easier to use the business data for analytics and insights. Employees at all levels can make data-driven decisions by accessing the insights through their dashboards.
Data analytics, data warehousing, and data engineering are different yet interlinked concepts used to streamline data collection, storage, and analysis in an enterprise. Statistics show that the global big data and data engineering market is expected to be $75.55 billion in 2024 and predicted to reach $169.9 billion by 2029 at a CAGR (Compound Annual Growth Rate) of 17.6%.
However, you may have questions about which service to use for your business. Should you hire data engineering services, or will it be enough to pay for third-party or embedded data analytics solutions? Where does data warehousing fit into the grand scheme of things?
Let’s find out in this article.
Is Data Warehousing the Same as Data Analytics?
A data warehouse is a central repository or a large database containing massive amounts of business-related data. It can be built on-premises or on the cloud platform. A data warehouse is connected to several internal and external sources as well as third-party applications like business intelligence tools, data analytical dashboards, etc. Data warehousing services include setting up the repository, building data pipelines, streamlining data flow, maintaining the database, and periodically upgrading the systems.
Data analytics is the process of converting raw data into actionable insights to make data-driven decisions. It helps see the hidden patterns, trends, and correlations in historical and present datasets. The insights derived are shared with end-users (employees) via data visualization dashboards. Data analytics help shape business processes to deliver better results while consuming fewer resources. It can be used to understand market trends, customer behavior, product performance, employee productivity, etc., and make the necessary changes to achieve business goals.
In short, data warehousing is not the same as data analytics. While the data warehouse is used to store and clean data, analytical tools help to understand what the data means and how it can help empower the business. Creating a synergy between data warehouse and data analytics will certainly give you the best results.
So, what is the difference between a Data Warehouse Engineer and a Data Analyst?
A data warehouse engineer is responsible for managing the entire development lifecycle of a data warehouse. It is a backend process that includes many activities, such as building the warehouse, system connections, ETL, performance management, resource management, dimensional design, etc. A data warehouse engineer works with data scientists, data analysts, and data engineers to ensure the data flow is smooth and seamless across the enterprise.
A data analyst uses the data stored in data warehouses and data lakes to review the information, detect patterns, and identify key insights useful for the business. The primary responsibility of a data analyst is to find solutions for various business problems by analyzing historical and real-time data and sharing insights with decision-makers. The data analyst has to collaborate with data warehouse engineers, software developers, and data scientists to run the data-driven model without interruptions or errors.
What are Data Engineering Services?
Data engineering encompasses various processes like data collection, data storage, data cleaning, and data analysis for large amounts of raw, structured, unstructured, and semi-structured data. It allows data scientists and data analysts to derive in-depth insights using various statistical and analytical methods. Data engineering also includes ensuring that the quality of the datasets is high to prevent inaccurate insights.
Data engineering services cover a broader area and include many responsibilities. For example, it can also include data warehousing solutions or a part of the warehousing processes. Typically, data engineering involves the following activities:
So, do data engineers do data warehousing?
Yes. Data engineers play a role in designing, developing, and maintaining the data warehouse and its connections. However, note that data warehousing services are only a part of data engineering responsibilities. The top data engineering companies provide end-to-end services, right from planning the strategy to maintaining and upgrading the relevant systems, tools, and processes in your business. Data engineers collaborate with other experts like software developers, data warehouse engineers, data scientists, and data analysts to create a robust data model in the enterprise.
Which is Better: Data Analytics or Data Engineering?
Despite the overlap in some processes and data being the common factor, there are quite a few differences between data analytics and data engineering services.
A business can invest in data analytical tools and derive insights to make important decisions. It can partner with a data analytics company to get embedded analytics through customized dashboards without setting up the IT infrastructure in the enterprise. Data analytics as a service is a cloud-based solution where third-party companies handle most of the backend work and share insights and reports with businesses.
Data engineering is much more complex and extensive than data analytics. Data engineering consulting companies build data pipelines, set up system integrations, build data warehouses/ data lakes, connect the necessary data analytics and business intelligence tools, and maintain proper data flow across the IT infrastructure. Programming, database management, and cloud computing are part of the services.
In today’s competitive scenario, investing in data engineering services is a better option than limiting your business to data analytics. This empowers you to unlock the full potential of data and gain an edge over competitors. It also keeps you one step ahead and capable of making proactive decisions to grab market opportunities or avoid pitfalls.
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Data Engineering Services vs. Data Warehousing vs. Data Analytics
As you can see, the three aspects are different but interconnected on many levels. If you want to convert your data into meaningful insights, you need to first collect the data from multiple sources. Then, you need to store it in a central repository. Next, you should process it using analytical tools. So, all three are equally important.
But what if you don’t have proprietary business data or the means to store large amounts of data? In such instances, you can opt for embedded data analytics solutions by third-party companies. The service providers will use public and paid data relevant to your industry to share real-time insights with your business. This is done by setting up embedded dashboards that can be accessed by your employees. Here, data management, compliance, etc., are the service provider’s responsibility. Additionally, businesses can hire only data analytics services to use the data in the departmental silos to derive insights. However, the results may not be as accurate or effective as you need them to be.
By going a step further, you can hire a data warehousing company to design, build, and maintain the central database for your organization. A data warehouse can be built on-premises or on a remote cloud server (public, private, or hybrid). For example, AWS, Azure, and Google Cloud offer applications to build and manage data warehouses and data lakes. This cloud setup will be completely managed by the service provider. It allows you to ensure data quality and use your proprietary data for analytics. While there will be some initial costs, it’s more of an investment and will give a high ROI over the years.
Data warehouse consulting companies provide customized strategies based on your requirements, budget, industry, etc., to ensure flexibility, scalability, and efficiency.
Data engineering takes things another step further by bringing together the entire IT infrastructure required to develop the data-driven model in an organization. Data engineering services strengthen the infrastructure and increase its security, scalability, flexibility, and agility. The companies handle each task, right from building data architecture to upgrading the systems periodically to optimize the processes as per your business requirements. If you hire data engineering services, you are also gaining access to tools, technology, and expertise for handling data warehouses and running data analytics.
Together, the three can increase the overall efficiency and effectiveness of your business processes and lead to employee and customer satisfaction.
In Conclusion
Data analytics, data warehousing, and data engineering are necessary for a business to adopt the data-driven model and gain a competitive edge in the market. While the best service depends on the requirements, business volume, and budget of an organization, opting for data engineering ensures you also have access to data warehousing and data analytical solutions.
Talk to a reputed data engineering company to hire end-to-end and tailored solutions for streamlining your systems and following the data-first approach to business. Use real-time insights to make proactive decisions and increase ROI.
FAQs
Do data analysts do ETL?
ETL (extract, transform, and load) is the process of collecting and cleaning data from multiple sources to load it into a central repository. Data analysts don’t do ETL. They only deal with the data after it has been through the ETL process. The ETL’s responsibility lies with data engineers. However, sometimes, a data analyst can be a part of the team that works on ETL and data warehousing activities.
Are ETL and data warehousing the same?
No. ETL is a part of the data warehousing services as it involves the collection, extraction, and loading of data from several sources into a central database. This database or repository is called a data warehouse. Data warehousing also deals with building a repository, setting up the data pipelines, maintaining the data, etc.
Is AWS a data warehouse?
AWS (Amazon Web Services) is not a data warehouse. It is a cloud platform offering an extensive suite of tools and technologies for complete data management. However, AWS offers a data warehouse as a service through Amazon Redshift, a fully managed data warehouse with cost-effective and quick services.
Is SQL Server a data warehouse?
Yes, SQL Server is a data warehouse belonging to Microsoft. It is a cloud-based service used for dimensional modeling, data vault modeling, data warehouse schemas, and other relevant processes to build, develop, and maintain a cloud-based data warehouse. It can be accessed as a part of data warehousing or data engineering solutions.
Originally published on DataToBiz