Downloaded 24 times
![© 2016 Progress Software Corporation and/or its subsidiaries or affiliates. All rights reserved.24 Python ODBC Example import pyodbc import getpass import sys def show_odbc(): sources = pyodbc.dataSources() dsns = sources.keys() sl = [] i = 1 for dsn in dsns: sl.append( str(i) + '. %s' % (dsn)) i= i+1 print('n'.join(sl)) return dsns def listTables(cursor): for row in cursor.tables(): print row.table_name def executeSelectQuery(cursor, cnxn): query = raw_input('Enter the SELECT Query:') cursor.execute(query)](https://image.slidesharecdn.com/sasanalyticsgrid-webinar-170503015235/75/Journey-to-SAS-Analytics-Grid-with-SAS-R-Python-24-2048.jpg)
![© 2016 Progress Software Corporation and/or its subsidiaries or affiliates. All rights reserved.33 Learn More about Data Access for SAS Analytics What DataDirect Does for SAS Shops “Taking R and Python from good to great with SAS” [Webinar hosted by SAS in April 17] Zencos Consulting Blog Tech Articles on configuring SAS with ODBC: • SAS/Access 9.4 interface to ODBC Tutorial across popular data sources such as SQL Server, Salesforce and Amazon Redshift • SAS/Access 9.4 interface to ODBC Tutorial across cloud data sources such as Marketo and Eloqua](https://image.slidesharecdn.com/sasanalyticsgrid-webinar-170503015235/75/Journey-to-SAS-Analytics-Grid-with-SAS-R-Python-33-2048.jpg)
Uploaded bySumit Sarkar
Journey to SAS Analytics Grid with SAS, R, Python
The document outlines the transition from traditional SAS analytics deployments to a grid-based approach utilizing SAS, R, and Python, emphasizing best practices and lessons learned during this modernization. It discusses the advantages of grid deployments, including scalability and improved administration, while detailing considerations for organizations ready to adopt high-performance computing solutions. Additionally, it highlights popular data sources and provides insights into implementing an open analytics strategy across various languages, supported by ODBC connectivity.
Journey to SAS Analytics Grid with SAS, R, Python
- 1. Journey to SAS AnalyticsGrid with SAS, R, Python Benjamin Zenick, Chief Operating Officer - Zencos Sumit Sarkar, Chief Data Evangelist - Progress DataDirect
- 2. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.2 Audio Bridge Options & Question Submission
- 3. Journey to SAS AnalyticsGrid with SAS, R, Python Benjamin Zenick, Chief Operating Officer - Zencos Sumit Sarkar, Chief Data Evangelist - Progress DataDirect
- 4. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.4 Agenda Differences between traditional and Grid deployments for SAS Best practices and lessons learned in deploying an Analytics Grid How to deliver an open analytics strategy for SAS, R, Python and others Popular data sources for advanced analytics
- 5. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.5 POLL WHERE ARE YOU IN YOUR ANALYTICS JOURNEY? DESKTOP ANALYTICS CLIENT/SERVER ANALYTICS GRID ANALYTICS CLOUD ANALYTICS OTHER
- 6. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.6 Differences between traditional and Grid deployments for SAS
- 7. The Evolution ofAnalytics Businesses started with large and expensive central mainframes – Mainframes were limited by early storage and processing technology – Connectivity and user interfaces to data were limited by “dumb” terminals – Expansion was limited by proprietary chassis design – Connecting multiple mainframes was expensive, challenging, or impossible
- 8. Analytics Today • Modernizationmoved away from Mainframes • Moved toward server / client solutions, workstations, storage appliances, and networking • Shortcoming of centralized datacenters: Administrative and Performance Bottlenecks
- 9. Example of TraditionalDeployment
- 10. What benefits dogrid deployments provide? • Standardization supporting multiple ecosystems • Streamline Administrative support • Better tools for analytics and administration • Centralizing and improving management • Size & Scalability
- 11. Example of GridDeployment
- 12. Signs your organizationis ready to consider an HPC or Grid solution… • Decrease in cost benefits • Current model doesn’t scale well • Massively Parallelized Processing • Administrative needs continue to grow and grow • High(er) Availability is possible • Faster (Disaster) Recovery Zencos capabilities prepared for TEST Co.
- 13. Top Considerations for“Modernization” • Why? • Who? • What? • Where? • When?
- 14. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.14 Best practices and lessons learned in deploying an Analytics Grid
- 15. Best Practices • Preparation •Technologies • Plan • Time • Expectations • Team • Transition • Users • Support • Goal Alignment
- 16. Lessons Learned • Investin a meaningful assessment • Plan to purchase and build Test and Disaster Recovery environments • Understand the applications and use cases • Outline support model for legacy projects • Consider your post-implementation needs • Expect the unexpected
- 17. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.17 How to deliver an open analytics strategy for SAS, R, Python and others
- 18. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.18 POLL WHICH LANGUAGE(S) ARE COMMONLY USED IN YOUR ORGANIZATION SAS Python R SPSS OTHER
- 19. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.19 SAS and Open Analytics across … SAS ViyaSAS Grid Manager SAS (open data access and grid management for native language support)
- 20. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.20 SAS Grid Manager Image from SAS webinar: https://www.evensi.us/webinar-taking-r-and-python-from-good-to- great-with-sas-/204358443
- 21. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.21 SAS with Open Data Access (ODBC) Access external data using supported access modes using data source specific SAS/Access interfaces. Leverage generic SAS/Access interface to ODBC with an open ODBC driver for direct access from Python and R.
- 22. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.22 Workers SAS and Open Analytics | SAS Grid (Open Data Access via ODBC) ODBC RDBMS, Big Data, NoSQL, Cloud Access data sources over TCP or HTTPS Analytics Grid Open Grid Manager Open Data Access Controller
- 23. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.23 R ODBC Example library(RODBC) # Make a connection using your DSN name conn <- odbcConnect("Spark Next") # Execute a SQL Tables call sqlTables(conn) # Execute a SQL columns call on the table with our energy data sqlColumns(conn, "energyconsumption") # Bind the results of a SQL query for plotting data <- sqlQuery(conn, "SELECT * FROM energyconsumption WHERE country IN ('China', 'United States', 'Canada', 'France', 'Germany', 'Italy', 'Japan')") # Attach the data for plotting access attach(data)
- 24. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.24 Python ODBC Example import pyodbc import getpass import sys def show_odbc(): sources = pyodbc.dataSources() dsns = sources.keys() sl = [] i = 1 for dsn in dsns: sl.append( str(i) + '. %s' % (dsn)) i= i+1 print('n'.join(sl)) return dsns def listTables(cursor): for row in cursor.tables(): print row.table_name def executeSelectQuery(cursor, cnxn): query = raw_input('Enter the SELECT Query:') cursor.execute(query)
- 25. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.25 DataDirect ODBC is engineered for GRID and Cloud Deliver advanced functionality over OSS to become SAS OEM Partner Run 85+ million QA tests on our suite of connectors Performance labs measure throughput and resource utilization (CPU and memory) Focus on security features for customers to achieve regulatory compliance
- 26. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.26 Popular data sources for advanced analytics
- 27. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.27 Popular Relational/Analytics Data Sources SQL Server 18.70% Oracle 12.89% MySQL 12.77% Progress OpenEdge 7.93% PostgreSQL 5.65% Microsoft SQL Azure 5.27% IBM DB2 4.76% SQLite 3.68% Teradata 2.61% SAP HANA 2.30% MariaDB 2.25% Sybase ASE 1.92% Amazon Redshift 1.79% Informix 1.64% Sybase IQ 1.30% Netezza 1.25% Other (please specify): 1.13% Amazon Aurora 1.00% Not sure 0.97% Pivotal Greenplum 0.87% Google BigQuery 0.77% Vertica 0.61%
- 28. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.28 Popular Big Data Sources Hadoop Hive 18.53% Spark SQL 8.17% Hortonworks 7.97% Cloudera CDH 7.87% Cloudera Impala 7.47% Apache Solr 7.37% Oracle BDA 6.67% Amazon EMR 5.98% Apache Sqoop 5.48% MapR 5.38% IBM BigInsights 4.68% Apache Storm 4.08% Apache Drill 2.39% Apache Phoenix 2.39% SAP Altiscale 2.19% Pivotal HD 1.89% Presto 0.80% GemFireXD 0.70%
- 29. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.29 Popular NoSQL Sources MongoDB 35.60% Cassandra 14.57% HBase 10.34% Oracle NoSQL 9.01% Redis 8.45% Other (please specify): 6.01% Couchbase 5.78% DynamoDB 2.78% DataStax Enterprise 2.22% SimpleDB 2.22% MarkLogic 1.67% Aerospike 0.78% Riak 0.56%
- 30. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.30 What about SaaS? Data Source API Eloqua Web Services API (REST/SOAP) Bulk and non-Bulk APIs No query language Oracle Service Cloud Web Services APIs (REST/SOAP) ROQL Google Analytics Hypercube (query limits of 10 metrics grouped by max of 7 dimensions) Veeva CRM SOAP, BULK, Metadata APIs SOQL
- 31. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.31 Supported ODBC Data Sources for SAS/Access Apache Hadoop Hive 0.8.0 and higher Amazon EMR 2.1.4 and higher Amazon Redshift Apache Spark SQL 1.2, 1.3, 1.4, 1.5 Cloudera CDH update 4 and higher Cloudera Impala 1.0, 1.1, 1.2, 1.3, 1.4 Cloudera Impala 2.0, 2.1, 2.2 Hortonworks 1.3 and higher IBM BigInsights 3.0 and higher MapR 1.2 and higher Pivotal HD 2.0.1 and higher DB2 V9.1, V9.5, V9.7, 9.8 for Linux, UNIX, Windows DB2 V8.x for LUW DB2 11 for z/OS* DB2 V10 for z/OS DB2 V9.1 for z/OS DB2 UDB V8.1 for z/OS DB2 I 7.1, 7.2* (DB2 UDB V7R1, V7R2 for iSeries) DB2 I 6.1 (DB2 UDB V6R1 for iSeries) DB2 for I 5/OS (DB2 UDB V5R4 for iSeries) Eloqua (Oracle Marketing Cloud) Financial Force Google Analytics Greenplum 4, 4.1, 4.2, 4.3 Greenplum 3.3 Hubspot Informix Dynamic Server 12.1* Informix Dynamic Server 11.0, 11.5, 11.7 Informix Dynamic Server 10.0 Informix Dynamic Server 9.2, 9.3, 9.4 Informix Dynamic Server 11.0, 11.5, 11.7 Informix Dynamic Server 10.0 Informix Dynamic Server 9.2, 9.3, 9.4 Marketo Microsoft Dynamics CRM 2011 Rollup 16, 2013, 2015 Microsoft SQL Server 2014* Microsoft SQL Server 2012 Microsoft SQL Server 2008 R1, R2 Microsoft SQL Server 2005 Microsoft SQL Server 2000 Desktop Engine (MSDE 2000) Microsoft SQL Server 2000 Microsoft SQL Azure* MongoDB 3.0 MongoDB 2.2, 2.4, 2.6 MySQL Enterprise Edition 5.0, 5.1, 5.5, 5.6* Oracle 12c R1 (12.1)* Oracle 11g R1, R2 (11.1, 11.2) Oracle 10g R1, R2 (10.1, 10.2) Oracle 9i R1, R2 (9.0.1, 9.2) Oracle 8i R3 (8.1.7) Oracle Service Cloud Oracle Sales Cloud Pivotal HAWQ 1.1*, 1.2* PostgreSQL 9.0, 9.1, 9.2, 9.3, 9.4* PostgreSQL 8.2, 8.3, 8.4 Progress OpenEdge 11.0, 11.1*, 11.2*, 11.3*, 11.4* Progress OpenEdge 10.1.x, 10.2.x Progress Rollbase 2.0 and higher* REST API (via OpenAccess) SAP Adaptive Server Enterprise 16.0* ServiceMax SugarCRM 7.1.6 and higher* Sybase Adaptive Server Enterprise 15.0, 15.5, 15.7 Sybase Adaptive Server Enterprise 12.0, 12.5, 12.5.x Sybase Adaptive Server Enterprise 11.9 Sybase IQ 16.0* Sybase IQ 15.0, 15.1, 15.2, 15.3, 15.4 Veeva CRM Blue text indicates cloud hosted Blue text* indicates cloud hosted with on-premises option
- 32. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.32 NEW cross data center access for SAS/Access interface to ODBC (over https) SAS/Access interface to ODBC
- 33. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.33 Learn More about Data Access for SAS Analytics What DataDirect Does for SAS Shops “Taking R and Python from good to great with SAS” [Webinar hosted by SAS in April 17] Zencos Consulting Blog Tech Articles on configuring SAS with ODBC: • SAS/Access 9.4 interface to ODBC Tutorial across popular data sources such as SQL Server, Salesforce and Amazon Redshift • SAS/Access 9.4 interface to ODBC Tutorial across cloud data sources such as Marketo and Eloqua
- 34. © 2016 ProgressSoftware Corporation and/or its subsidiaries or affiliates. All rights reserved.34 Wrap Up with Q&A Slides and recording will be made available to each attendee Visit www.datadirect.com to learn more about ODBC drivers engineered for analytics Please enter your questions in the chat...
Editor's Notes
- #2 Can Your Current Infrastructure Support High-Performance Analytics and Data Science? Big data, compliance and a highly skilled workforce are driving organizations to transform their current analytical infrastructure to deliver enterprise computing environments that can support the latest in data science and analytics practices. SAS remains a popular choice for statistical programming languages, but there is growing demand for R and Python. Data engineers are now being tasked to deliver scalable and highly available computing resources to support analytics for a growing number of users and increasing data volumes while maintaining security for their customers. Join this webinar to learn: Differences between traditional and Grid deployments for SAS Best practices and lessons learned in deploying an Analytics Grid How to deliver an open analytics strategy for SAS, R, Python and others Popular data sources for advanced analytics
- #3 Join Audio: 2 ways to do so, 1) to use VoIP, click on “Mic & Speakers”, or 2) to use your telephone, click on “telephone” and dial-in using the numbers and information provided 2) All lines are muted for today’s webinar. We do plan to have a live Q&A session at the end of the presentations. However if you have a question at any time during this webinar, simply submit your questions via the “Question” section of the webinar interface located to the right of your screen – we will collect all questions through this “Question Window”. Final Note: we are recording today’s webinar