creating a modern data architecture

Creating a modern data architecture September 28, 2016

Ben Sharma | CEO

[email protected]

•  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

3 Zaloni Proprietary

Increased Agility

New Insights

Improved Scalability

Data lakes are central to the modern data architecture


•  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


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


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:


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



Sensors (or other time series data)

Relational Data Stores (OLTP/ODS/

DW)

Logs(or other unstructured

data)

Social and shared data


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


•  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


Raw Zone Refined Zone

Trusted Zone

Sandbox

Data Lake


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



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


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


Trusted Zone

Sandbox

Data Lake

•  Standardized on corporate governance/ quality policies


•  Metadata catalog available to all

•  Single version of truth

Trusted Zone



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:



•  De tokenization if required (based on access)

Refined Zone


Trusted Zone

Sandbox

Data Lake


Refined Zone





Inputs:

•  Output from Raw, Trusted and Refined Zones •  Self-service ingestion

Sandbox


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:






Sandbox


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


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


•  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


•  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


•  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


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


•  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


•  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


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


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

= + +


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!

creating a modern data architecture

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