Hadoop Data Lake Trends and Predictions

Mark van Rijmenam Founder Datafloq

Big Data Strategist International Keynote speaker

Author Think Bigger

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 analytics Within an open and secure environment

Democratize Access to Data

6 Important Characteristics of a Hadoop Data Lake

BigDataLake

DataSource&Latencydemands

Agnos7c

Dataisstoredinna7veformat

BuildwithHadoop

framework

Role-basedAccesstoAllData

Mix&matchanydatasource

Completeflexibilityand

extremelyscalable

Prediction 2015: Time to Experiment with Data Lakes

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

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

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

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

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

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

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

The future of Big Data: Data-Lake-as-a-Service Solutions

Data-Lake-as-a-Service

Image: EMC

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

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

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

Thank you

@vanRijmenam

https://datafloq.com

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