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Walking around the
Data Lake
Fatima Naqui
Sr Big Data Engineer at
Fidelity Investments
Naqui.Fatima@gmail.com
linkedin.com/in/fatimanaqui
Agenda
• What is a Data Lake?
• Why do I need a Data Lake?
• High Level Architecture
• Principles of a Data Lake
• Data Lake layers and components
• Ingestion
• Metadata management
• Data Lineage and Governance
• Data Curation
• Technical Architectures from Cloud providers
• Q&A
What is a
Data Lake?
Gartner’s definition of a Data Lake
“A data lake is a collection of storage instances of
various data assets. These assets are stored in a
near-exact, or even exact, copy of the source
format and are in addition to the originating data
stores.”
Why do I need
a Data Lake?
• Cost effective way to deal with big data
challenges
• Single source of truth that can be reused
• Cheaper and flexible alternative to batch
workloads
• Increase analytics and operational agility
High level
architecture
Principles of a
Data Lake
• Separation of storage and compute
• Bridging information silos
• Schema on read
Data Lake layers and
components
Ingestion
• Land everything without modification
• Dump information from source
systems
• Structured, unstructured, historical
data
Metadata Management
Foundation for
distributed data
management
Supports operational use
of data
Data semantic variations
over time
Monitoring changes in
data usage
Data Lineage and Governance
Data lineage deals with data's origins
Where data moves over time?
What happens to the data?
How is the data transformed?
Data governance is the process of
managing
Availability
Usability
Security
Integrity
Data Curation
Optimize data from its raw
form to be made consumable
Source of insights Enrich the data with analytic
and algorithmic functions
Technical
Architectures
Cloud
Architectures
Cloud
Architectures
Q & A

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Walking Around the Data Lake

  • 1. Walking around the Data Lake Fatima Naqui Sr Big Data Engineer at Fidelity Investments Naqui.Fatima@gmail.com linkedin.com/in/fatimanaqui
  • 2. Agenda • What is a Data Lake? • Why do I need a Data Lake? • High Level Architecture • Principles of a Data Lake • Data Lake layers and components • Ingestion • Metadata management • Data Lineage and Governance • Data Curation • Technical Architectures from Cloud providers • Q&A
  • 3.
  • 4. What is a Data Lake? Gartner’s definition of a Data Lake “A data lake is a collection of storage instances of various data assets. These assets are stored in a near-exact, or even exact, copy of the source format and are in addition to the originating data stores.”
  • 5. Why do I need a Data Lake? • Cost effective way to deal with big data challenges • Single source of truth that can be reused • Cheaper and flexible alternative to batch workloads • Increase analytics and operational agility
  • 7. Principles of a Data Lake • Separation of storage and compute • Bridging information silos • Schema on read
  • 8. Data Lake layers and components
  • 9. Ingestion • Land everything without modification • Dump information from source systems • Structured, unstructured, historical data
  • 10. Metadata Management Foundation for distributed data management Supports operational use of data Data semantic variations over time Monitoring changes in data usage
  • 11. Data Lineage and Governance Data lineage deals with data's origins Where data moves over time? What happens to the data? How is the data transformed? Data governance is the process of managing Availability Usability Security Integrity
  • 12. Data Curation Optimize data from its raw form to be made consumable Source of insights Enrich the data with analytic and algorithmic functions
  • 16. Q & A