It is indisputable that the analysis of large databases has become a trend. Big Data has been considered as a key factor and priority in the plan of expansion of many companies. As it grows, an increasingly larger database is also needed, and this is where the data lake is very relevant.
The role of the data lake is precisely to house a colossal volume of native data, from the most diverse sources. Many consider this database as garbage, and it is a fact that not all information will have the same relevance, but it is indisputable that with the right tools one can extract valuable and varied insights and analysis.
The fact that the data is unreliable raises another point. Data scientists always tend to question the validity of information, and having raw data allows you to apply custom techniques and models for each purpose, rather than working with already processed and made-up data.
How to implement a Snowflake Data Lake?
The first big step is to identify a map of your data and identify where the Data Science team can start to get insights, it’s not a simple answer, mainly because the Snowflake Data Lake focuses on bringing all the data, not reproducing the same Data Warehouse scenario. After defining the architecture, where to start the development? What is the first data? Where do I start taking the value? With that defined, we started to develop integrations that are already starting to provide this data to the Data Science team, so the structure starts to pay for itself. The first big step is to understand where the data resides and understand what data can bring value, and then start bringing that data into Lake. Or simply you can take the help of India Snowflake Companies to connect and access the Snowflake Data lake.
How much does a Snowflake Data Lake project cost?
It depends on your repository strategy, the number of integrations, the skill of your team, so it is not a value you can measure as a product, Data Lake is an architectural structure, it would be analogous to asking how much it is costs microservices, for example. Without an analysis, without checking which adapters are, where this data is, storage, how this data will be consumed, it is difficult to infer cost.
Is it a trend for Data Warehouse to be replaced by Data Lake?
Structures can coexist, companies that only have Data Warehouse should be thinking about Data Lake, but you can have the two structures coexist without any problem. You can even use your lake as a source for your Data Warehouse, you can use your Data Lake as a framework for Data Science and use your Data Warehouse for Analytics, BI, Self-Service BI for business users to develop dashboards and indicators. The great value of Data Lake is that you provide this structure for all your data and do not limit your Data Science view, one thing does not exclude the other. Companies spend around 80% of their time preparing data and only 20% doing analysis, that is, a lot of time is spent preparing something that often ends up not being used. The data lake overcomes this disadvantage as it has no predefined template. As it stores native data, no time is wasted on useless processing, analytics are only done if the data is used, bringing flexibility to the use of the data.