The document discusses emerging trends and predictions for Hadoop data lakes, highlighting the characteristics such as flexibility, scalability, and the ability to handle multiple data sources. It emphasizes the rise of IoT data lakes, the integration of Spark for enhanced analytics, and the development of data-lake-as-a-service solutions to address challenges in governance and security. Overall, the document forecasts a promising future for data lakes in big data analytics.

1. Hadoop Data Lake Trends and Predictions

2. Mark van Rijmenam
Founder
Datafloq
Big Data Strategist
International Keynote speaker
Author Think Bigger

3. Data Lakes: Store Anything, Analyze Everything
Ingest and store
multiple data
streams
From a variety of
data sources
Enabling real-time
in-memory &
in-database analyticsWithin an open and
secure environment
Democratize Access to Data

4. 6 Important Characteristics of a Hadoop Data Lake
Big Data Lake
Data Source &
Latency demands
Agnos7c
Data is stored
in na7ve format
Build with
Hadoop
framework
Role-based
Access to
All Data
Mix & match
any data
source
Complete
flexibility and
extremely scalable

5. Prediction 2015:
Time to Experiment with Data Lakes

6. The Growth in Data and Advantages of Data
Lakes results in increase in Data Lakes

7. Let’s look ahead and discuss the a few
Data Lake Trends for 2016

8. 1) The Rise of IoT Data Lakes
2) Spark Enabled Data Lakes
3) The Appearance of Data-Lake-as-a-Service

9. 500 Billion connected devices need a good data
storage & analytics solution: the IoT Data Lake

10. The Challenges of the Internet of Things are the
drivers behind the IoT Data Lake
Shared Standards
and Infrastructure
Data Control
and Access
Data
Security
The IoT
Data Lake

11. Spark will Ignite Analytics on your Data Lake
Store = Hadoop & Analytics = Spark

12. There are several important components of
Apache Spark
General Purpose Engine
Large-scale data
processing
In-memory distributed
computing engine
Build quickly,
iterate fast
Unified
platform

13. The future of Big Data:
Data-Lake-as-a-Service Solutions
Data-Lake-as-a-Service
Image: EMC

14. Three important challenges solved with
Data-Lake-as-a-Service Solutions
Governance
Data
Preparation
Metadata
Management
Security
Data
Breaches
Role-based
Access
Value
Reduce
Complexity
Reduce Costs
Improve
Analytics

15. The three Data Lake trends will lead to new
approach of Big Data

16. There is bright future ahead of Data Lakes. Let
Kelly discuss examples and use cases

17. Thank you
@vanRijmenam
https://datafloq.com
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