Traditional data warehouse vs data lake

ENTERPRISE DATA
WAREHOUSE VS DATA LAKE
Bhaskar Chaudhury
02-May-2016
Comparing transformed, purified and bottled water with open natural water

Objective
• Concept of Data Warehouse
• Business value of Data Warehouse
• Concept of Data Lake
• Business value of Data Lake
• Major differences
• Business Data Lake Architecture
• Key Benefits of Business Data Lake over DW

Data Warehouse - Concept
• Data Warehouse is a structured repository of non-volatile,
subject oriented, integrated, non-operational and time variant
data accumulated from multiple heterogeneous sources such
as relational database, flat files, etc.
• Data Warehouse stores data in files or folders and helps to
organize and use the data to take strategic decisions
• The important functions related to Data Warehouse are Data
Extraction, Data Cleaning, Data Transformation, Data Loading
and Refreshing.
• Data Warehouse provides summarized and multi-dimensional
view of atomic and summary data
• Data Warehousing is the process of transforming data into
information and make it available to decision makers in
timely manner

Data Warehouse – Business Value
• A good number of useful management reports can be
generated from the carefully designed data model in DW
using BI tools
• Based on data stored in DW pre-defined reports and metrics
can be generated to measure performance of business units
• Presence of DW makes it easy to view drill down details
underlying the summaries in reports and dashboards
• Data Warehouse makes it much easier to provide secure
access to those that have a legitimate need to specific data
and to exclude others
• Data Warehousing provides opportunity to uncover hidden
insights for decision making
• Since the DW eliminates the need for BI tools to compete with
transaction processing, users can analyse data faster and
generate reports more efficiently

Data Lake - Concept
• A Data Lake is a large-scale storage repository that holds vast
amount of raw data in its native format until it is needed
• Data Lake stores large quantities and varieties of structured,
semi-structured and un-structured data from various sources
• Each data element in a lake is assigned a unique identifier and
tagged with a set of extended metadata tags
• A Data Lake is a horizontally scalable data store that processes
large volumes and/or variety of data.
• Data lake characteristics often include fast ingest/write
speeds and low-cost storage, as they are designed to manage
high-volume, high-velocity raw data
• Data lakes have widely-varied analytic capabilities
• "Data cloud" is an emerging term that many companies use in
different ways but generally refers to a cloud deployment of
Data Lake

Data Lake – Business Value
• Data Lake improves Customer intimacy by providing 360
degree view of customer
• Data Lake helps in better Risk Management activity in areas
like Fraud detection
• Using Data Lake analysts can traverse through the data and
move, transform and create analytical sandboxes on-demand
to determine the 'integration value' of the information that
lives in the data
• Data Lake creates new business opportunities by providing
service like Data as a Product
• Data Lake increases the amount of data being analysed and
operationalized within the business – turning insight into
action
• Data Lake helps to increase top line revenue

Major Differences
• Data Lake will retain all data whereas Data Warehouse may
remove insignificant data to conserve space. This is because
hardware for a Data Lake usually differs significantly from that
used for a Data Warehouse
• Data Lake stores all data types regardless of source and
structure in raw form and transform them when needed. In
Data Warehouse only extracted, transformed and structured
data is stored
• In the Data Lake, since all data is stored in its raw form and is
always accessible to someone who needs to use it, users are
empowered to go beyond the structure of the Warehouse to
explore data in novel ways and derive faster insights
• Data Lake supports all user types like operational, analysts,
data scientists whereas Data Warehouse mostly support
operational users

Key Benefits of Data Lake over DW
• Scalability – It is the capability of a data system, network, or process
to handle a growing amount of data or its potential to be enlarged
in order to accommodate that data growth
• Converge All Data Sources – Data Lake has ability to store logs, XML,
multimedia, sensor data, binary, social data, chat and people data
• Advanced Analytics – Data Lake excels at utilizing the availability of
large quantities of coherent data along with deep learning
algorithms to recognize items of interest that will power real-time
decision analytics
• Accommodate High Speed Data – In order to have the high speed
data in the Data Lake, it should use few of the tools like Chukwa,
Scribe, Kafka, and Flume which can acquire and queue the high
speed data. By leveraging this high speed data can integrate with
the historical data to have its fullest insights
• Defer labor-intensive Schema development and data clean-up –
until an organization has identified a clear business need. Data lakes
are more suitable for the less-structured data

Traditional data warehouse vs data lake

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