The Data Lake From 10,000 Feet

Pradeep Menon has a high-level explanation of what a data lake is and how it differs from traditional data warehouses:

With the changes in the data paradigm, a new architectural pattern has emerged. It’s called as the Data Lake Architecture. Like the water in the lake, data in a data lake is in the purest possible form. Like the lake, it caters to need to different people, those who want to fish or those who want to take a boat ride or those who want to get drinking water from it, a data lake architecture caters to multiple personas. It provides data scientists an avenue to explore data and create a hypothesis. It provides an avenue for business users to explore data. It provides an avenue for data analysts to analyze data and find patterns. It provides an avenue for reporting analysts to create reports and present to stakeholders.

The way I compare a data lake to a data warehouse or a mart is like this:

Data Lake stores data in the purest form caters to multiple stakeholders and can also be used to package data in a form that can be consumed by end-users. On the other hand, Data Warehouse is already distilled and packaged for defined purposes.

One way of thinking about this is that data warehouses are great for solving known business questions:  generating 10K reports or other regulatory compliance reporting, building the end-of-month data, and viewing standard KPIs.  By contrast, the data lake is (among other things) for spelunking, trying to answer those one-off questions people seem to have but which the warehouse never seems to have quite the right set of information.

Related Posts

Loading Data Into SnowflakeDB

Dan Bilsborough shows a couple ways of loading data into SnowflakeDB from Azure: Before being loaded into a Snowflake table, the data can be optionally staged, which is essentially just a pointer to a location where the files are stored. There are different types of stages including:– User stages, which each user will have by […]

Read More

Finding Queries to Cache In-App

Brent Ozar provides guidance on the types of queries you might want to cache in your application: Question 2: Will out-of-date data really hurt? Some data absolutely, positively has to be up to the millisecond, but you’d be surprised how often I see data frequently queried out of the database when the freshness doesn’t really matter. […]

Read More

Categories

August 2017
MTWTFSS
« Jul Sep »
 123456
78910111213
14151617181920
21222324252627
28293031