Presentation from Data Science Conference 2.0 held in Belgrade, Serbia. The focus of the talk was to address the challenges of deploying a Data Lake infrastructure within the organization.

1. Milos Milovanovic, Co-Founder & Data Engineer @ Things Solver
milos@thingsolver.com
milos@datascience.rs
Planning and Optimizing
Data Lake Architecture

2. Agenda
Introduction - Business Data Requirements
What is A Data Lake?
A Common Data Lake Architecture
When Problems Start To Show Up - Optimizing Data Lake
Expanding a Data Lake
How To Plan Data Lake - Success Factors

3. Introduction - Business Data Requirements
Main goal for organizations is to adapt and put all of their data into use.
It’s not an easy task - it might require the mindset and structural changes.
Flexibility and agility are required for success.
Various trends and buzzwords are making it hard to stay on track.
Challenge of Transforming Enterprise Data Management - (“The data lake is a
foundational component and common denominator of the modern data architecture
enabling, and complementing specialized components, such as enterprise data
warehouses, discovery-oriented environments, and highly-specialized analytic or
operational data technologies…” - John O’Brien, CEO @ Radiant Advisors)

4. Data Lake - The Very First Definition
“If you think of a datamart as a store of bottled water – cleansed and packaged
and structured for easy consumption – the data lake is a large body of water in a
more natural state. The contents of the data lake stream in from a source to fill
the lake, and various users of the lake can come to examine, dive in, or take
samples.”
- James Dixon, CTO @ Pentaho

5. A More Formal Definition
“A data lake 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.”

6. Data Warehouse & Data Lake

7. Data Warehouse & Data Lake by Example
Social Media Streaming can be implemented using traditional Data Warehouse
… but such an application will be to restricted and inflexible (extending the
number of columns analyzed).
Using Data Lake for this purpose gives us flexibility to adapt and test new metrics
… and we can easily add new applications on top.

8. Data Lake Architecture Overview

9. A Common Data Lake Implementation Architecture
❏ In general, the architecture of a data lake is simple: a Hadoop File System
(HDFS) with lots of directories and files on it.
❏ Hadoop is usually in the center of Data Lake Architecture, although the concept
is broader than Hadoop.
❏ Hadoop’s scalable, low-cost persistence layer and its ability to perform big data
processing and analytics is a great toolset to achieve measurable business value
opportunities at speed and low cost.
❏ Hive and Spark provide us rich analytics on top of the data that is persisted at
low cost.

10. This Architecture:
Acts like SQL
Efficient and Scalable
Connects to Basically Anything
Different Processing Modes
(Realtime, Batch, Pipelines, Machine
Learning, Ad Hoc Analysis …)
HADOOP
DISTRIBUTED
FILE
SYSTEM
HIVE AND SPARK
DATA SOURCES

11. When Problems Show Up
Hadoop + Spark/Hive != Database
- Searching a row within TBs of Data
select * from my_table where some_column like ’%123asd%’;
- No updates and deletes
- Too many concurrent requests from BI Tools
...
Spark Best Practice: http://go.databricks.com/not-your-fathers-database

12. How Do We Optimize Such a Solution?
❏ Use ORC File Format
❏ File Compaction (small files, deduplication)
❏ Run Spark on YARN
❏ Use Spark Dataframes
❏ Data Caching
❏ Use Traditional Databases
❏ Extend the Toolset (Solr, ES, Kafka, Redis, …)

13. Data Lake - Extended Toolset
HDFS
AND MANY MORE...

15. How To Start With The Data Lake?
❏ Think of the Use Cases (don’t plan all the use cases - have some in mind)
❏ Master the Technology
❏ Go agile and flexible
❏ Do not forget about the Data Governance, Data Quality, Security (but do not
drown in this)
❏ Integrate with BI and DWH

16. Make data accessible and let Data
Scientists go fishing in the Lake.

17. Milos Milovanovic, Co-Founder & Data Engineer @ Things Solver
milos@thingsolver.com
milos@datascience.rs
Planning and Optimizing
Data Lake Architecture