An abiding lesson is here to stay – businesses will always run-on data. After all, companies want to know their customers better and take informed actions at fast speeds that accelerate their growth. However, as data’s volume, velocity, and variety grow exponentially, it is easier said than done.
The challenges of creating and managing data warehouses
For one, there is a matter of time. Cloud-based data lakes are for those situations where businesses need faster and less expensive access to data (instead of creating a warehouse that could take multiple months and millions of dollars). Then there is the matter of cost – of the man-efforts and storage. While the benefits of analyzing data for seeing trends and determining cause-and-effect patterns aren’t lost on businesses, only a few can think of storing 24X7 data for their search queries. Lastly, it is a matter of complexity. Enterprises dedicating teams for prepping and maintaining systems for data analysis is one thing, but provisioning personnel that handles data movement, transformation, allocating schema definitions, and management (for each use case) is another complexity.
Data Warehouses work when –
There are, of course, situations where data warehouses work better. Specificity is one. Data warehouses are the go-to solution when projects are launched with exact questions and intended outcomes. Next is the matter of scale, when hundreds and possibly thousands of users need data access for use cases. Lastly, data warehouses are desirable when the frequency of access is predictable and cyclic.
5 Reasons to dive into a data lake.
Growth in time-series data.
With the rise of IoT devices, there is an increase in time-series databases. Not only do these engines have specific data models and query languages, but also, they are optimized for certain types of datasets. When such massive sensor data has to be managed, data lakes work out inexpensive compared with the curated data warehouses. However, such a decision should be taken after due diligence and stakeholder alignment, and realistic expectations are served.
Higher business maturity and clarity in use cases.
In the past years (and accelerated after COVID), many industry leaders have realized that their shift toward big data architectures equips them with game-changing capabilities. In the scenarios where they have identified the highest-value use cases for big data, the executives speak about the profound benefits that data lakes bring. There are many benefit areas – real-time risk and fraud alert monitoring and IT performance optimization.
Availability of multiple operating models.
When selecting the use cases, it is essential to clarify the operating models that best suit data lakes.
The operating model best suited for a data lake is a ‘transformation‘ model when RDBM systems are phased out of customer, product, and business insight-generating functions. Then there is the ‘complement’ model – when a data lake alongside a data warehouse supports use cases that traditional data warehouses don’t fulfill. A ‘replacement’ model is when a data lake replaces parts of the existing data warehouse solution. This step allows for cheaper storage and reduced processing costs. The last operating model is ‘outsourcing’ when companies adopt cloud technologies and reduce their CAPEX for infrastructure and specialist skills. This helps them leverage analytics as a service by having vendors process their data and receive insights in return.
Mainstreaming of data virtualization practices.
Today the multitude of challenges with data lakes (replicating data, GDPR data security, and data governance) are being solved with data virtualization. Accessing data in place as and when needed rather than moving to another location, organizations are incorporating data virtualization in their data lake implementations. The data virtualization practices integrate data sources across multiple data types and locations, leaving the end-user with a single logical layer. This unifies data governance and security controls, bringing a higher success rate for data lake implementations.
Growth of Industry 4.0
The agile IT architecture needed for Industry 4.0 necessitates using data lakes. As fragmented in-house IT architecture gives way to homogeneity and various connections between data cubes, the importance of data lakes are underscored. More than the pilot projects run today, as the different use cases of Industry 4.0 report higher profitability margins, data lakes with external data integration capabilities would become the go-to standard – for flexibility, security, and higher ecosystem collaborations.
Conclusion
Data lakes are stepping out of the shadow of data warehouses. New developments and business value are reported increasingly because two powerful shifts have merged – computing power and massive data amounts.
To realize data’s full potential, more businesses will embrace the data lake approaches equipped with robust governance approaches, multi-tiered data usage, management models, and innovative delivery methods.
