How to build effective data lakes

Global data will reach 175 million petabytes within the next five years.

Source IDC

Varied data sources and formats

Data lakes consolidate and secure data from different sources and formats into a single repository for easy access and analysis.

STEP 1

Setting up a data lake

Depending on the use case, data lakes can be built on object storage or Hadoop. Both can scale and seamlessly integrate with existing enterprise data and tools.

STEP 2

Ingesting big data

All this data in varied formats must be ingested and prepared for users to leverage.

A data lake is a flexible storage mechanism for storing raw data.


60%By 2025, nearly 60% of existing data will be created and managed by enterprise organizations (compared to 30% in 2015).

STEP 3

Exploring all data

Data catalogs are essential to users’ ability to search and find data to analyze.

Data catalogs with machine learning present current datasets and relevant data through optimized metadata management.


99.5%
of collected data remains unused, primarily due to a lack of infrastructure, resources, and management.

STEP 4

Analyzing all data

Analyzing data from a single source limits insights. Data lakes consolidate data from multiple sources, including data from across the business, enabling users to analyze all available data.


5data sources are consulted on average to analyze and reach a data-driven decision

STEP 5

Accelerating time to decisions

Leveraging data lakes, machine learning, and analytics capabilities, users gain intelligent insights that inform data-driven business decisions.

Data lakes from Oracle

For a data lake to be effective, an organization must examine its specific governance needs, workflows, and tools. Building around these core elements creates a powerful data lake that seamlessly integrates into existing architectures and easily connects data to users.

The result?

Streamlined processes

Faster access

Powerful insights

How to build effective data lakes

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Global data will reach 175 million petabytes within the next five years.”

Source IDC

Varied data sources and formats

Data lakes consolidate and secure data from different sources and formats into a single repository for easy access and analysis.

STEP 1

Setting up a data lake

Depending on the use case, data lakes can be built on object storage or Hadoop. Both can scale and seamlessly integrate with existing enterprise data and tools.

STEP 2

Ingesting big data

All this data in varied formats must be ingested and prepared for users to leverage.

A data lake is a flexible storage mechanism for storing raw data.


60%By 2025, nearly 60% By 2025, nearly 60% of existing data will be created and managed by enterprise organizations (compared to 30% in 2015).
Source: IDC Data Age 2025

STEP 3

Exploring all data

Data catalogs are essential to users’ ability to search and find data to analyze.

Data catalogs with machine learning present current datasets and relevant data through optimized metadata management.


99.5%of collected data remains unused, primarily due to a lack of infrastructure, resources, and management.
Source: Grow.com

STEP 4

Analyzing all data

Analyzing data from a single source limits insights. Data lakes consolidate data from multiple sources, including data from across the business, enabling users to analyze all available data.


5Data
Sources
are consulted on average to analyze and reach a data-driven decision
Source: Bi-Survey.com

STEP 5

Accelerating time to decisions

Leveraging data lakes, machine learning, and analytics capabilities, users gain intelligent insights that inform data-driven business decisions.