Tuesday, July 17, 2018

Data Lake Vs Data Warehouse

We know that data is the business asset for any organisation which always keeps secure and accessible to business users whenever it required. 
In current era, two techniques are very popular to store the data for the business insights. Hence, we are going to differentiate them based on some technical terms.

One is Data Warehouse which is highly structured store of the data that is requiring a significant amount of discovery, planning, data modeling, and development work before the data becomes available for analysis by the business users.

Second one is a Data Lake which is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data. The data structure and requirements are not defined until the data is needed. We can say that Data Lake is a more organic store of data without regard for the perceived value or structure of the data.

Data lakes are a big opportunity to store large amounts of data in an affordable way without having to decide upfront how it must be structured and used. They are typically used to complement traditional data warehouses, which are still better adapted for highly-trusted, tightly-governed data such as your financial figures, but there are some overlaps between the two compositories.

Data Warehouses compared to Data Lakes - Depending on the business requirements, a typical organization will require both a data warehouse and a data lake as they serve different needs, and use cases.
Data Warehouse
Data Lake
Type of data stored
Structured data (most often in columns & rows in a relational database) from transactional systems, operational databases, and line of business applications
Any type of data structure,
any format, including structured, semi-structured, and unstructured data from IoT devices, web sites, mobile apps, social media, and corporate applications
Best way to ingest data
Batch processes
Streaming, micro-batch, or
batch processes
Designed prior to the DW implementation (schema-on-write)
define the structure of the data at the time of analysis , referred to as schema on reading (schema-on-read)
Typical load pattern
ETL - (Extract, Transform, then Load)
ELT - (Extract, Load, and Transform at the time the data is loaded)
Fastest query results using higher cost storage
Query results getting faster using low-cost storage
Data Quality
Highly curated data that serves as the central version of the truth
Any data that may or may not be curated (ie. raw data)
Business analysts
Data scientists, Data developers, and Business analysts (using curated data)
Analytics pattern
Determine structure, acquire data, then analyze it; iterate back to change structure as needed.
Batch reporting, BI and visualizations
Acquire data, analyze it, then iterate to determine its final structured form.
Machine Learning, Predictive analytics, data discovery and profiling
During the development of a traditional data warehouse, we should decide a considerable amount of time which is going to spend analyzing data sources, understanding business processes, profiling data, and modeling data.
In contrast, the default expectation for a data lake is to acquire all of the data and retain all of the data.
Please visit us to learn more on -
  1. Collaboration of OLTP and OLAP systems
  2. Major differences between OLTP and OLAP
  3. Data Warehouse - Introduction
  4. Data Warehouse - Multidimensional Cube
  5. Data Warehouse - Multidimensional Cube Types
  6. Data Warehouse - Architecture and Multidimensional Model
  7. Data Warehouse - Dimension tables.
  8. Data Warehouse - Fact tables.
  9. Data Warehouse - Conceptual Modeling.
  10. Data Warehouse - Star schema.
  11. Data Warehouse - Snowflake schema.
  12. Data Warehouse - Fact constellations
  13. Data Warehouse - OLAP Servers.
  14. Preparation for a successful Data Lake in the cloud
  15. Why does cloud make Data Lakes Better?

1 comment:

  1. Good one, would have been better if the page was responsive..viz., readable on mobile