At Gartner Data & Analytics Summit 2017 Barry Zane, Vice President of Engineering, and Ben Szekely, Vice President of Solutions, discussed how Cambridge Semantics' Anzo Smart Data Lake® empowers business users with on-demand analytics on rich data through the use of graph database technology. These are the slides from their presentation.

1. Anzo Smart Data Lake™ - Accelerating Insight
Disrupting the Analytics Time-to-Value Function
Barry Zane
Vice President, Engineering
barry@cambridgesemantics.com
Ben Szekely
Vice President, Solution Engineering
ben@cambridgesemantics.com

2. ©2017 Cambridge Semantics Inc. All rights reserved.
Big Data and Analytics Industry Trends
• We are graduating from pieced-together ETL, Hadoop and BI solutions
to consolidate around complete end-to-end solutions
– Forward thinking customers looking to product vendors for innovation, delivery
and accountability for value.
– Consolidation of partnerships and acquisitions

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Cloud Computing Trends
• Cloud Computing is a transformative cost saver for analytics as demand
for access to all data grows
– Think beyond infrastructure balance sheet savings
– Pay only for the analytics compute you use, as business needs demand and peak.

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The importance of “Time-to-Value”
• Time-to-Value from data becoming the key driver for analytics strategy
with an assumption of self-serviceEffort
Time to Value

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Key Risks
Costs rising from vendor lock-in of data format/storage,
analytics tools and cloud infrastructure.

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Anzo Smart Data Lake: Accelerating Insight
Disparate Sources
Insight
Exploratory Analytics
Knowledge Discovery
Data on Demand
Automated
Ingestion
Enterprise Knowledge Graph
Governance

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ITBuildandDeployment
Anzo Smart
Data Lake
AddNewDataAddNewData
AddNewDataAddNewData
Disrupting the Time-to-Value Function
TimeandResource
Investments
Insights and Value
Anzo Smart
Data Lake

8. ©2017 Cambridge Semantics Inc. All rights reserved.
Anzo Smart Data Lake
A Graph-based Platform to Disrupt the Analytics Time-to-Value Function
Connectors Models Rules
Analytics &
Tools
ASDL Customer Fingerprint - Intellectual Property
Data Ingestion
& Mapping
Automated
ETL Generation
Collaborative
Mapping
Text Processing
Data
Cataloging
Data & Model
Governance
Active Metadata
Management
Role-Based Security
Discovery &
Analytics
Automated Query
Generation
User Dashboards
and Custom UI/UX
Self-Serve Live
Extracts
In-Memory MPP
Query
Graphmarts
on Demand
ELT, Model Based
Data Integration
Document Search
Actionable
Insights
Enterprise
Data Sources
Enterprise
Data Lakes
“Last Mile”
Analytics

9. ©2017 Cambridge Semantics Inc. All rights reserved.
Data Ingestion
& Mapping
Automated
ETL Generation
Collaborative
Mapping
Text Processing
Data
Cataloging
Data & Model
Governance
Active Metadata
Management
Role-Based Security
Discovery &
Analytics
Automated Query
Generation
Custom User
Dashboards
Self-Serve Live
Extracts
In-Memory MPP
Query
Graphmarts
on Demand
ELT, Model Based
Data Integration
Document Search
Actionable
Insights
“Last Mile”
Analytics
Elastically Scaled Analytics
Scalable Encrypted Storage
Anzo Smart Data Lake – Cloud Deployment
ASDL cloud deployment in Amazon Web Services or Google Cloud Platform
Cloud automation is a significant and strategic component of the Cambridge
Semantics roadmap including deployment, elastic scale and high-availability. Our
cloud mission is to offer customers lower costs in development, maintenance and
operations – using cloud resources efficiently as business needs determine.
Enterprise
Data Lakes
Enterprise
Data Sources
Elastically Scaled Ingestion
Cloud-delivered ASDL offers faster deployment and on-demand scale

10. Large Scale Graph Analytics
Graph is a simple, clean model for standard analytic queries and allows you to do more.
But, using Graph has had terrible performance for standard analytics queries against large-scale data.
If you can’t do the standard “data warehouse” queries at scale, you won’t get to the algorithms that only Graph can perform!
Build a Graph engine designed for large-scale analytics.
Leverage parallel computing - lots of hardware. Scale to hundreds of severs.
Extend the SPARQL language to backfill functionality present in SQL.
Deploy thru a user interface that automatically writes the SPARQL, and visualizes the results.
PROBLEM
SOLUTION

11. Analytic Landscape
ROLAP - Relational online analytics
•Broad adoption, 45 years of technology evolution
•Based on declarative SQL for business analysts
•Formal ANSI/ISO standard since 1986
GOLAP - Graph based online analytics
•Narrow adoption, accelerating over past 15 years
•Based on declarative SPARQL for business analysts
•Formal W3C standard since 2008
Hadoop (Spark) - Offline batch analytics
•Growing adoption since created in 2005 (2012)
•All queries programmed in Java/Scala/Python…
•Apache and community standards
•Limited only by programmer’s talents and available APIs

12. GOLAP is Real Relational Data Warehouse, Really
Relational Databases are predefined “rectangular” tables and rows with columns.
–Very natural for subjects (aka rows) with a number of known attributes common to all/most
of the subjects.
–Allows columns to be links (aka keys) to other table’s subjects.
Challenged by:
–Sparsity
–One-to-many needs a separate “join table”
–You need to understand the data in advance
Graphs are real relational, really. Just a little different than the points
above!

13. RDF/SPARQL… like RDB/SQL, but...
Standard SQL aggregates, joins, etc, but simple and powerful relationship capabilities.
“How is Joe related to Mary”
–In SQL Relational
•Are they spouses?
•Are they siblings?
•Are they friends?
•Do they have the same hobby?
•… enumerate the choices, EXPLODES with degrees of separation
–In SPARQL Graph
•How is Joe related to Mary?
•… you can directly specify degrees of separation
Pretty exciting, essentially all the power of SQL, but you can do more, with more diverse data, where the data
tells you about itself, rather than you knowing in advance.

14. The Smart Data Lake is the “database”
• Data cached in HDFS, AWS/GCP buckets
• Multiple Graph Query Engine instances, usually on subsets
• Ephemeral in-memory operation
• Short term instances - load, query, toss

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