The Evolution of the Data Landscape - Sas Institute · 2019-05-20 · MS SQL Server MySQL...
Transcript of The Evolution of the Data Landscape - Sas Institute · 2019-05-20 · MS SQL Server MySQL...
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The Evolution of the Data Landscape
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IDSIMS
System R (SQL)IngresOracle
InformixDB2Sybase ASETeradataRed Brick
JADEInterSystemsVersant InformixDB2 OracleMS SQL ServerMySQLPostgreSQL
HadoopClouderaMapRMongoDBExadataNetezzaHP NeoviewGreenplum
Hortonworks, Cloudera, MapR, Snowflake, S3, MariaDB, Amazon Redshift, DynamoDBDocumentDB, Amazon Neptune, Amazon Timestream, Oracle, DB2, ArongoDB, MS Azure SqlServer, PostreSQL, MySQL, Neo4J
1960 1970 1980 1990 2000 2010 Today
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Customers / Partners
Cloud Platform
Social Media
Mobility / Devices & IoT
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Data inconsistencies
Data SilosData
Protection/Security
Skill shortage
Data Wrangling
Data LatencyDuplicate
Data
Moving Data to the Cloud
An evolving Data Landscape introduces challenges
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What is a Data Fabric?
“Data Fabric is an architecture and set of data services that provide consistent capabilities across a choice of endpoints spanning on-premises and multiple cloud environments”
- NetApp
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What is a Data Fabric?
“Data fabric is a combination of architecture and technologythat is designed to ease the complexities of managing many
different kinds of data, using multiple database management systems, and deployed across a variety of platforms”
- Eckerson Group
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What is a Big Data Fabric?
“Big data fabric is an emerging platform which accelerates business insights “by automating ingestion, curation, discovery,
preparation and integration from data silos”
- Forrester Research
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Why Data “Fabric”?Fabrics are interconnected structures where multiple
nodes appear as a single logical unit
What makes up a Data Fabric?1. Technology / Data Services2. Architecture
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Data Fabric
Data Lake
Data Streams
EDW
Cloud DW
New Data
Cloud Applications
Data Management
On-Premises Off-Premises
Data Management
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Forrester Data Fabric Reference Architecture
Data Security
Governance
Metadata
Search
Data Quality
Lineage
Data Management
Caching, In-Memory, Embedded, Self-Service
Data modeling, Preparation, Curation, Virtualization
Transformation, Integration, Cleansing
Ingestion, machine learning, streaming, data movement
NoSQL
RDBMS
Hadoop
Data access
Data discovery
Orchestration
Processing and persistence
Data Ingestion
On-premises sources Cloud sources
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Data Scientists
Challenges
• Understanding what data is available
• Getting access
• Putting the data to use• Data preparation
• Analytics
• Decisioning
• Connected, always-on data
“Is this the best data available?”
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Data Engineers
Challenges
• Modernize & simplify data infrastructure
• Optimize use of storage & compute
• Leverage new technologies
• Safe movement of data to the cloud
“We can make this run faster”
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CIO and CDO
Challenges
• Simplify and Modernize Data infrastructure
• Protect the data
• Compliance
• Data Governance
• Reduce Infrastructure Costs
“Data is secure and protected”
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HYPEREALITY
sas.com
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