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• Award-winning provider of enterprise data lake management solutions:
Integrated data lake management platform
Self-service data preparation
• Data Lake Design and Implementation Services: POC, Pilot, Production, Operations, Training
• Data Science Professional Services
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Increased Agility
New Insights
Improved Scalability
Data lakes are central to the modern data architecture
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• Store all types of data in its raw format
• Create Refined, Standardized, Trusted datasets for various use cases
• Store data for longer periods of time to enable historical analysis
• Query and Access the data using a variety of methods
• Manage streaming and batch data in a converged platform
• Provide shorter time-to-insight with proper data management and governance
The data lake promise
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Data architecture modernization Tr
aditi
onal
M
oder
n
Data Lake
Sources ETL EDW
Derived (Transformed)
Discovery Sandbox EDW
Streaming
Unstructured Data
Various Sources
Data Discovery Analytics BI
Data Science Data Discovery
Analytics BI
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Data lake – the challenges and the solution
• Ingestion
• Lack of Visibility
• Privacy and Compliance
• Quality Issues
• Reliance on IT
• Reusability
• Rate of Change
• Skills Gap
• Complexity
Managing: Delivering: Building:
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Data Lake Reference Architecture
• Data required for LOB specific views - transformed from existing certified data
• Consumers are anyone with appropriate role-based access
• Standardized on corporate governance/ quality policies• Consumers are anyone with appropriate role-based access• Single version of truth
Transient Landing Zone Raw Zone
Refined Zone
Trusted Zone
Sandbox
Data Lake
• Temporary store of source data
• Consumers are IT, Data Stewards
• Implemented in highly regulation industries
• Original source data ready for consumption
• Consumers are ETL developers, data stewards, some data scientists
• Single source of truth with history
• Data required for LOB specific views - transformed from existing certified data
• Consumers are anyone with appropriate role-based access
Sensors (or other time series data)
Relational Data Stores (OLTP/ODS/
DW)
Logs(or other unstructured
data)
Social and shared data
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Inputs:
• Sources: RDBMS, File, Streaming, Structure/Unstructured, External Data
Processes:
• Data transfer and intake: Managed and scheduled • Discover metadata
• Register in the catalog • Apply Zone specific policies
• Capture operational metrics and monitoring • Post-ingestion validations and clean up
Outputs:
• Data transfer to Raw Zone
Policies:
• Data privacy – tokenization, masking • Data security – user access
• Data quality – profiling, entity level checks • Data lifecycle management – short lived, temporary
Transient Landing Zone
Transient Landing Zone
• Temporary stores source data
• Limited access
• Consumers are IT, Data Stewards
• Implemented in highly regulation industries
• This zone collapses with Raw Zone if security is not needed
Transient Landing Zone
Raw Zone Refined Zone
Trusted Zone
Sandbox
Data Lake
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Inputs:
• Output from TLZ (Hcatalog entries) • Source inputs if TLZ is skipped
Policies:
• Data privacy – tokenization, masking • Data security – user access
• Data Quality – profiling, entity level, field level • Data transformations required for Single View of Truth
• Combine attributes to one entity • Change data formats (e.g. VSAM binary of JSON) • Derived columns • Field mappings • Drop columns
• Data lifecycle management • Could be a candidate for S3 or Object store
Outputs:
• Data transfer to Trusted Zone • Data transfer to Sandbox
Processes:
• Register and update catalog • Apply zone specific policies
• Operational metrics and monitoring
Raw Zone
Transient Landing Zone
Raw Zone Refined Zone
Trusted Zone
Sandbox
Data Lake
Raw Zone
• Original source data• Ready for consumption• Treated for basic
validation and privacy• Metadata available to
everyone but data access limited based on role
• Consumers are ETL developers, data stewards, some data scientists
• Single source of truth with history
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Inputs:
• Output from Raw Zone
Processes:
• Register and update catalog • Apply zone specific policies
• Data transformation required for refined use cases for LOB such as
• Customer360 view • Periodic snapshots of revenue
Outputs:
• Data transfer to Refined
Policies:
• Data security – user access • Data lifecycle management
• Lifetime of use case • Use case specific
Trusted Zone
Raw Zone Refined Zone
Trusted Zone
Sandbox
Data Lake
• Standardized on corporate governance/ quality policies
• Consumers are anyone with appropriate role-based access
• Metadata catalog available to all
• Single version of truth
Trusted Zone
Transient Landing Zone
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Inputs:
• Output from Trusted Zone • Output from Raw Zone for LOB-specific use cases
Processes:
• LOB specific transformations • Aggregates
• De-normalized
• Apply zone specific policies • Model building for reports
• Optionally a cube generation
Outputs:
• Transformed data can be saved back to Refined Zone
• Applications such as BI tools • Transferred to sandbox if required
Policies:
• Data security – user access • Data lifecycle management
• Lifetime of use case • Use case specific
• De tokenization if required (based on access)
Refined Zone
Raw Zone Refined Zone
Trusted Zone
Sandbox
Data Lake
Transient Landing Zone
Refined Zone
• Data required for LOB specific views - transformed from existing certified data
• Consumers are anyone with appropriate role-based access
• Metadata catalog available to all
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Inputs:
• Output from Raw, Trusted and Refined Zones • Self-service ingestion
Sandbox
Raw Zone Refined Zone
Trusted Zone
Sandbox
Data Lake
Transient Landing Zone Processes:
• Data scientists drive analysis • Self-service for ad-hoc
Outputs:
• Models that can later be operationalized • Optionally, results/data can be sent back to
the Raw Zone
Policies:
• Data security – user access • Data lifecycle management
• Lifetime of use case • Use case specific
• Data required for LOB specific views - transformed from existing certified data
• Consumers are anyone with appropriate role-based access
• Metadata catalog available to all
Sandbox
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Data lake Reference Architecture with Zaloni
Consumption ZoneSource System
File Data
DB Data
ETL Extracts
Streaming
TransientLanding Zone Raw Zone
Refined Zone
Trusted Zone
Sandbox
APIs
Metadata Management
Data Quality Data Catalog Security
Data Lake
Business AnalystsResearchers
Data Scientists
DATA LAKE MANAGEMENT & GOVERNANCE PLATFORM
Sensors (or other time series data)
Relational Data Stores (OLTP/ODS/
DW)
Logs(or other unstructured
data)
Social and shared data
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Data Lake 360 ° : A holistic approach to actionable big data
1. Enable the lake
2. Govern the data
3. Engage the business
• Foster a data-driven business through self-service data discovery and preparation
• Safeguard sensitive data and enable regulatory compliance
• Improve data visibility, reliability and quality to reduce time-to-insight
• Leverage the full power of a scale-out architecture with an actionable, scalable data lake
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• Managed Ingestion § Ability to ingest vast amounts of data
§ Ability to handle a wide variety of formats (streaming, files, custom) and sources
§ Build in repeatability through automation to pick up incoming data and apply pre-defined processing
• Metadata Management § Capture and manage operational, technical and business metadata
§ Provides visibility and reliability – key to finding data in the lake
§ Reduced time to insight for analytics
§ File and record level watermarking provides data lineage, enables audit and traceability
Enable the lake
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• Data Lineage § See how data moves and how it is consumed in the data lake.
§ Safeguard data and reduce risk, always knowing where data has come from, where it is, and how it is being used.
• Data Quality
§ Rules based Data validation
§ Integration with the Managed Data Pipeline
§ Stats and metrics for reporting and actions
Govern the data
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• Data Security and Privacy § Differing permissions require enhanced data security
§ Mask or tokenize data before published in the lake for consumption
§ Policy-based security
• Data lifecycle management across tiered storage environments
§ Hot -> Warm -> Cold on an entity level based on policies/SLAs
§ Across on-premise and cloud environments
§ Provide data management features to automate scheduling and orchestration of data movement between heterogeneous storage environments
Govern the data
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Engage the business
• Data Catalog § See what data is available across your enterprise
§ Contribute valuable business information to improve search and usage
§ Use a shopping cart experience to create sandbox for ad-hoc and exploratory analytics
• Self-service Data Preparation § Blend data in the lake without a costly IT project
§ Perform interactive data-driven transformations
§ Collaborate and share data assets and transformations with peers
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• Rapid increase of Data Lake platforms in the Cloud
• Hybrid cloud and multi-cloud considerations
• Support sensitive data on premise and external data in the cloud (e.g. client data, machine-generated)
• Key challenges:
§ Leverage Cloud native features
§ Consistence Data Management and Governance
Emergence of cloud-based and hybrid data lakes
GOVERNANCE
VISIBILITY
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• How do you create a cloud agnostic data lake platform?
• How deploy a cost-effective compute layer? § Elastic compute layer § Batch and near real-time
• How do you optimize storage? § Support polyglot persistence § DLM
• How do you optimize network connectivity between Ground to Cloud?
• How do you meet enterprise security requirements?
Considerations for data lake in the cloud
CLOUD and HYBRID ENVIRONMENTS
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Cloud Data Lake Maturity model
Lift and Shift
Cloud Native features
Multi and Hybrid Cloud
Replicate on-premise Data Lake in the cloud
Leverage Object stores, Transient compute platforms, Messaging systems
Abstraction over multiple clouds, consistent Data Management and Governance
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Building your blueprint
1. Questions 2. Inputs 3. Outcomes
Business Drivers AND Business Questions: e.g. Where is fraud occurring? How do I optimize inventory?
Data Use Cases Platform
Subject Areas Source System
Capabilities, Process
Ingest, Organize, Enrich, Explore
Roadmap
Managed Data Lake
Analytics Strategy
= + +
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Typical data lake implementation timeline
POC
Weeks Weeks
Production Data Lake Platform
Proof of Concept: ü Demonstrate technical
capabilities of the platform in the context of selected use cases
Data Lake Implementation: ü Planning, Installation, Training
ü Sample data sets ingested
ü Pilot uses cases created
Business Use Case Delivered: ü Engage business
stakeholders to identify production use cases at scale
ü Review learnings and optimize the data lake
Data Lake Use Case Implement Business Use Case
Varies byUse Case
DATA LAKE MANAGEMENT AND GOVERNANCE PLATFORM
SELF-SERVICE DATA PREPARATION
FREE T-SHIRT!
Building a Modern Data ArchitectureBen Sharma, CEO and Founder, Zaloni
Wednesday, 2:05 p.m. – 1 E 09
Demo and FREE copy of book
“Architecting Data Lakes”
Speaking Sessions: Cloud Computing and Big Data
Ben Sharma, CEO and Founder, Zaloni Tuesday, 9:30 a.m. – 1B 01/02
Visit Booth #644 for these giveaways!