dashDBthe GIS professional’s bridge to mainstream IT systems

John ParkProduct ManagerIBMjjpark@ca.ibm.com

Hi IBM … what have you been up to lately ?

Why am I here, and what am I going to talk about ?

dashDB and ArcGIS supportShow you stuff

©2015 IBM Corporation 

Supporting Applications at Web Scale

Fast, fully managed, cloud data warehouse service Integrated analyticsR predictive modeling Geo, Open and Twitter Data SupportBuilt-in RStudio IDE

Built-in performance with in-memory technologyPredictive modeling built into the database (linear regression, k-means clustering, Esri compatible, and more)Works with an ecosystem of apps and toolsIntegrated security and maintenance

IBM dashDB

©2015 IBM Corporation 

For apps that need:• Elastic scalability• High availability• Data model flexibility• Data mobility• Text search

• Geospatial

Available as:• Fully managed DBaaS• On-premises private cloud• Hybrid architecture

BLU Acceleration

Netezza In-Database

Analytics

Cloudant NoSQL

Integration

dashDB MPP

Fully-managed data warehouse on cloud DB2 BLU columnar technology +

Netezza in-database analytics BLU in-memory processing, data skipping, actionable

compression, parallel vector processing, , “Load & Go” administration

Netezza predictive analytic algorithms, fully integrated RStudio & R language

Oracle compatibility Massively Parallel Processing (MPP)

dashDBIn-database analytics capabilities for best performance atop a fully-managed warehouse

©2015 IBM Corporation 

BLU is better

©2015 IBM Corporation 

Massively Parallel Platform

IBM NetezzaIn-Database

Analytics

Transformations

Geospatial

Predictive

Statistics

Data Mining

More ToolsIn-Database

Analytics

SAS

R

Fuzzy Logix

Zementis

IBM SPSS

BI Tools

Visualization Tools

SoftwareDevelopment

Kit

User-DefinedExtensions(UDF, UDA,

UDTF, UDAP)

LanguageSupport

(Map/Reduce, Java, Python,

Lua, Perl,C, C++,

Fortran)

Custom Stored Procedures (NZPLSQL)

BigInsights

Fuzzy Logix

Streams

Apache Hadoop

Mathematics

Time Series

IBM Netezza Analytics at a Glance

©2015 IBM Corporation 

dashDB Key Use Cases

• Extend on-premises data warehouse environments to the cloud

• Flexible, cost-effective growth• Hybrid cloud models support ground to

cloud

Extend / Modernize

• Easy synchronization of JSON to structured data

• Allows analytics via standard BI tools• In-database predictive algorithms allow

greater insight for Cloudant users than ever before

JSON Analytics

• Robust predictive analytic algorithms• Integrated with R• Watson Analytics ready• Analytics ecosystem with partners

In-Database Analytics

• Data warehousing and analytics in the cloud• Cloud agility and flexibility• Analytics for cloud data, data marts, and

development & test environments

Data Warehouse & Analytics

Service

©2015 IBM Corporation  9

©2015 IBM Corporation 

Use Case: Car Manufacturer - Architecture Details

Vehicle Data

Other Data Sources

IBM Cloudant

GeoJSON data to relational

Cloudant Schema Discovery Protocol

IBM dashDB

IBM Dataworks

Enterprise MPP

• Dedicated, single tenant environment• Bare metal

• 3+ node clusters

• 24 cores per node• 256 GB memory per node• SSD storage (for about 4 TB of

preload data per node)

• $5410 / month (USD) per node

Entry

• Shared, multi-tenant environment

• 20 GB SAN storage capacity

• Freemium: < 1 GB of raw data is free

• $50 / month (USD) flat rate

Enterprise - 1TB

• Dedicated, single tenant environment

• Virtual environment

• 16 cores• 64 GB memory• SAN storage (for about

1 TB of preload data)

• $1170 / month (USD)

Enterprise - 4 TB

• Dedicated, single tenant environment

• Bare metal

• 32 cores• 256 GB memory• SAN storage (for

about 4 TB of preload data)

• $4700 / month (USD)

Enterprise - 12 TB

• Dedicated, single tenant environment

• Bare metal

• 32 cores• 256 GB memory• SAN storage (for about

12 TB of preload data)

• $7370 / month (USD)

dashDB Plans Suit a Variety of Needs

Geospatial Analytics In dashDBImplements OGC SFS & ISO SQL/MM part 3 standards for spatial

See http://www.iso.org/iso/catalogue_detail.htm?csnumber=38651Spatial data type ST_GEOMETRY (hierarchy)Enables spatial operations (e.g. joins) in database through spatial operators available as user defined functionsDedicated support in ESRI tools starting V 10.3dashDB - R support through extension to ibmdbR package

Spatial Functions & Predicates in dashDB

SELECT a.name, a.type FROM highways a, floodzones b WHERE ST_Intersects(a.location,b.location) = 1 AND b.last_flood > 1950

SELECT a.road_id, a.time, i.id, ST_Distance(a.loc, i.loc,’METER’) as distanceFROM accidents a, intersections i WHERE ST_Distance(a.loc,i.loc,’METER’) < 10000 AND a.weather = ‘RAIN’

- accidents near intersections

- highways in flood zones

ST_Distance(g1,g2)

?

ST_Intersects(g1,g2)

?

Spatial Constructor Functions

ST_Point(x, y, srs_id) – create point at this location

ST_Point(‘POINT (-121.5, 37.2)’, 1)

ST_Linestring(‘LINESTRING (-121.5 37.2,-121.7 37.1)’,1)

ST_Polygon(CAST (? AS CLOB(1M)),1)For host variable containing well-known text, well-known binary, or shape representation

Spatial Predicates – WHERE Clause

ST_Distance(geom1, geom2) < distance_constant or var

ST_Contains(geom1, geom2) = 1

ST_Within(geom1,geom2) = 1

EnvelopesIntersect(geom1, geom2) = 1

EnvelopesIntersect(geom1, x1, y1, x2, y2, srs_id) = 1

ST_Area(geom) < some_value

Spatial Functions that Create New Spatial Values

ST_Buffer(geom, distance)

ST_Centroid(geom)

ST_Intersection(geom1, geom2)

And Many More …

ST_AreaST_AsBinaryST_AsTextST_BoundaryST_BufferST_CentroidST_ContainsST_ConvexHullST_CoordDimST_CrossesST_DifferenceST_DimensionST_DisjointST_DistanceST_EndpointST_EnvelopeST_EqualsST_ExteriorRingST_GeomFromWKBST_GeometryFromTextST_GeometryN

ST_GeometryTypeST_InteriorRingNST_IntersectionST_IntersectsST_IsClosedST_IsEmptyST_IsRingST_IsSimpleST_IsValidST_LengthST_LineFromTextST_LineFromWKBST_MLineFromTextST_MLineFromWKBST_MPointFromTextST_MPointFromWKBST_MPolyFromTextST_MPolyFromWKBST_NumGeometriesST_NumInteriorRingST_NumPoints

ST_OrderingEqualsST_OverlapsST_PerimeterST_PointST_PointFromTextST_PointFromWKBST_PointNST_PointOnSurfaceST_PolyFromTextST_PolyFromWKBST_PolygonST_RelateST_SRIDST_StartPointST_SymmetricDiffST_TouchesST_TransformST_UnionST_WKBToSQLST_WKTToSQLST_WithinST_XST_Y

And more…

Simplified Constructors from x,y WKT WKB GML shapeLinear referencingSpatial aggregationST_AsGMLST_AsShape

Harness the Full Power of SQLOuter joinCommon table expressionsRecursive queries, sub-queriesAggregate functionsOrder by, group by, having clausesOLAP, XML, and more ...

WITH sdStores AS (SELECT * FROM stores WHERE st_within(location, :sandiego) = 1)SELECT s.id, s.name, AVG(h.income) FROM houseHolds h, sdStores sWHERE st_intersects(s.zone, h.location) = 1GROUP BY s.id, s.nameORDER BY s.name

Example problem: Determine the average household income for the sales zone of each store in the San Diego area.

GeoData & dashDB

{GeoJSON}

WKT((),())

WKB

GML

Let me show you something

Merci Beaucoup ! dashDB.com

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