Data Vault Vs Data Lake

Sep 26, 2019Download as PPTX, PDF1 like1,732 views

Calum Miller

The difference between a Data Lake and a Data Vault is the difference between a stethoscope and a radar.

What do we do?
Signal Processing or Data
Processing?
• Signals start conversations
• Signals move boardrooms
• Signals release IT expenditure
• Signal variety, reliability and context
are key business drivers
• Data Processing ends
conversations!

Signal Processing is the
customer of Data
Integration & Warehousing
Signal
Processing
Business
Intelligence
Artificial
Intelligence
Reporting Analytics
Spreadsheets
Dashboards

Sales are down but why?
There are many interpretations of
reality;
• Website broken
• Marketing budget cut
• Campaign poor
• Product price uncompetitive
• New product release
• Company trashed by Trump
• Fashion victim
• Delivery delays and/or cost
• Recession

Signal Processing at Scale
• The Cloud is one massive signal
processor, with limitless
compute power and storage
• The Role of Data Integration in
the cloud is the organisation of
data sets for both efficient and
effective signal processing
• Data Lakes & Vaults have
emerged as key cloud
integration patterns

Data Lake Evolution
• 2011: Horton Works Forms
• 2012: AWS announces Amazon RedShift
• 2014: Data Lake European on premise
projects take off
• 2015: Snowflake released on AWS
• 2015: Hive and Presto released on AWS
• 2017: AWS Athena released
• 2006: Amazon AWS Launches
• 2008: Yahoo Open Sources Hadoop
• 2009: Cloudera Forms
• 2009: AWS Elastic MapReduce
• 2010 (October): Apache Hive release
• 2010 (October): James Dickson,
CTO Pentaho, coined the term Data Lake

Data Lake Signals are Isolated
• Data Lakes encourage detailed
analysis of a very narrow field
• Thinking across separate data
sources is difficult and inconsistent
• A silo mentality can emerge
• Data Scientists spend their time
hunting for the data lake ontology
• Weak support for strategic
decisions
• Too easy to make bad decisions on
limited data

Data Lake Warning
The danger with Data Lakes is that they encourage
decisions based upon what can be easily measured

Data Lakes are Good for
• Starting EDW projects
• Persistent staging areas
• Feedstock for Data Vaults
• Tactical Analysis
• DWH flexibility
• API Calls/Gateway
• Unstructured log analysis
• Operational Monitoring

Data Vault Evolution
• 1990s: Conceived by Dan Linstedt
• 2000: DV 1.0 Released into public domain
• 2014: DV 2.0 Announced

Data Vault Trends
• Strong tools are emerging for source centric
modelling and model population
• The need for business centric modelling
• Patterns emerging for automation of documentation,
validation and reconciliation
• New Data Warehouse Databases complement data
vaults
• GDPR and & PII are driving the need for ontologies
• S3/Athena as a Data Vault?

Data Vaults are Good for
• EDW projects
• Strategic Analysis
• Feedstock for Cubes and Models

Data Vault Signals are related
through business context
Sales are down and here is the
business context
• Broadens the field of vision and
the scope of questions
• Increases the variety, quality and
strength of signal channels
• Different business perspectives
are supported in a consistent
analysis framework

Leaders need situational
awareness
Data Vaults expose relationships between different
business signals

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Watch full webinar here: https://bit.ly/3JBpwGm Data lakes and data warehouses offer organizations a centralized data delivery platform. From the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and that 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important. In the recent report Logical Data Fabric to the Rescue Integrating Data Warehouses, Data Lakes, and Data Hubs by Rick van der Lans, we also discovered the importance of “time to insight and speed”. During this webinar, we will discuss how a logical data fabric not only helps organizations have a holistic view of their data across multiple data lakes, data warehouses, and data sources but how it improves time to value. Catch this on-demand session & learn: - How a Logical Data Fabric is the right approach to assist organizations to unify their data. - The advanced features of a Logical Data Fabric that assist with optimizing your queries irrespective of data source, whether the data is in a data lake, data warehouse, or other sources. - How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.

The Great Lakes: How to Approach a Big Data ImplementationInside Analysis

- Rick Stellwagen from Think Big, A Teradata Company, discussed best practices for implementing a data lake including establishing standards for data ingestion and metadata capture, developing a security plan, and planning for data discovery and reporting. - Analyst Robin Bloor asked questions about metadata management, data governance, and security for data lakes. Bloor noted that while data lakes are a new concept, best practices are needed as organizations move analytics and BI capabilities to this model. - Upcoming Briefing Room topics in 2015 will focus on big data, cloud computing, and innovators in technology.

Bridging the Last Mile: Getting Data to the People Who Need It (APAC)Denodo

Watch full webinar here: https://bit.ly/34iCruM Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it. Attend this session to learn: - The challenges organizations face when trying to get data to the business users in a timely manner - How Data Virtualization can accelerate time-to-value for an organization’s data assets - Examples of leading companies that used data virtualization to get the right data to the users at the right time

Low-Latency Analytics with NoSQL – Introduction to Storm and CassandraCaserta

Businesses are generating and ingesting an unprecedented volume of structured and unstructured data to be analyzed. Needed is a scalable Big Data infrastructure that processes and parses extremely high volume in real-time and calculates aggregations and statistics. Banking trade data where volumes can exceed billions of messages a day is a perfect example. Firms are fast approaching 'the wall' in terms of scalability with relational databases, and must stop imposing relational structure on analytics data and map raw trade data to a data model in low latency, preserve the mapped data to disk, and handle ad-hoc data requests for data analytics. Joe discusses and introduces NoSQL databases, describing how they are capable of scaling far beyond relational databases while maintaining performance , and shares a real-world case study that details the architecture and technologies needed to ingest high-volume data for real-time analytics. For more information, visit www.casertaconcepts.com