At this point in 2019, it is quite obvious that the technology landscape is changing rapidly. One hot topic that is impacting every industry and business unit is the facelift that data warehouses of the past are going through. While technology updates are a common occurrence, drastic changes in concepts like the pivot to cloud based modern data warehouses that boast immense functionality improvements are rare and extremely impactful to say the least. We could talk about the benefits of a cloud data warehouse all day but today we want to highlight the pitfalls of data warehouses of the past to exemplify the need for change and answer the new big question, “Is our data warehouse holding us back?”
Exponential Data Growth
As reliance upon technology in general continues to rise so does the accompanying data it produces. This exponential data growth has been best articulated by the doubling of the worlds data every two years. Being able to scale traditional on prem data warehouses and accompany at this rate is not only labor intensive but extremely costly.
Varying Data Sources
Data is growing, not only in size but in original data source as well. More and more cloud based applications, data as a service, and 3rd party public data sources are all being consumed to analyze and uncover new trends almost every day. Traditional data warehouses can only handle certain bandwidths without latency and data backup.
Varying Data Types
As the sources grow, so does the structure of the data. These different formats often require data manipulation work to even ingest let alone analyze these datasets. Many traditional databases are only able to handle structured data and in a world where IoT devices, for example, capture anything but structured data an advancement that can handle noSQL is essential.
Need Access Faster
As Compute and Storage is one size fits all when referencing traditional data warehouses it can become troublesome when a spike in user activity demands data access all at once. Being able to ramp up compute and or storage as needs arise is a must for large organizations if data agility with low latency is a top priority.
Cost of Management
Beyond latency issues, data warehouses of the past require large upfront investments to ramp up data initiatives and handle new workloads. With data science being exploratory in nature, it is often extremely difficult to get budget approval for such projects, even though the payoff may be astronomical. Modern data warehouses make it much cheaper and cost effective to scale up and down compute and storage as new needs and questions arise arise and fall.
To sum everything up; Data warehouses of the past are an expensive data storage solution incapable of handling complex data sources, types and sizes all while boasting no scalability and custom pricing options. To learn more about Modern Cloud Data Warehouses and how they can fundamentally transform your organization, please reach out to email@example.com. If the problems above sound familiar, the answer to “Is your data warehouse holding you back” is simply, yes.
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