Building a data pipeline to ingest data into Hadoop in minutes using Streamsets Data Collector

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Slides from my talk at the Hadoop User Group Ireland meetup on June 13th 2016: building a data pipeline to ingest data from sources of different nature into Hadoop in minutes (and no coding at all) using the Open Source Streamsets Data Collector tool.

Datos y análisis

Building a data pipeline to ingest
data into Hadoop in minutes
using Streamsets Data Collector
Guglielmo Iozzia,
Big Data Infrastructure Engineer @ IBM Ireland

Data Ingestion for Analytics: a real scenario
In the business area (cloud applications) to which my team belongs there were so
many questions to be answered. They were related to:
● Defect analysis
● Outage analysis
● Cyber-Security

“Data is the second
most important
thing in analytics”

Data Ingestion: multiple sources...
● Legacy systems
● DB2
● Lotus Domino
● MongoDB
● Application logs
● System logs
● New Relic
● Jenkins pipelines
● Testing tools output
● RESTful Services

… and so many tools available to get the data

What are we going to do with all those data?

Issues
● The need to collect data from multiple sources introduces redundancy, which
costs additional disk space and increases query times.
● A small team.
● Lack of skills and experience across the team (and the business area in
general) in managing Big Data tools.
● Low budget.

Alternatives
#3 Find a smart way to simplify the data ingestion
process

A single tool needed...
● Design complex data flows with minimal coding and the maximum flexibility.
● Provide real-time data flow statistics, metrics for each flow stage.
● Automated error handling and alerting.
● Easy to use by everyone.
● Zero-downtime when upgrading the infrastructure due to logical isolation of
each flow stage.
● Open Source

Streamsets Data Collector: supported origins

Streamsets Data Collector: available destinations

Streamsets Data Collector: available processors
● Base64 Field Decoder
● Base64 Field Encoder
● Expression Evaluator
● Field Converter
● JavaScript Evaluator
● JSON Parser
● Jython Evaluator
● Log Parser
● Stream Selector
● XML Parser
...and many others

Streamsets DC: performance and reliability
● Two available execution modes: standalone or cluster
● Implemented in Java: so any performance best practice/recommendation for
Java applications applies here
● REST services for performance monitoring available
● Rules and alerts (metric and data both)

Streamsets Data Collector: security
● You can authenticate user accounts based on LDAP
● Authorization: the Data Collector provides several roles (admin, manager,
creator, guest)
● You can use Kerberos authentication to connect to origin and destination
systems
● Follow the usual security best practices in terms of iptables, networking, etc.
for Java web applications running on Linux machines.

Useful Links
Streamsets Data Collector:
https://streamsets.com/product/

Thanks!
My contacts:
Linkedin: https://ie.linkedin.com/in/giozzia
Blog: http://googlielmo.blogspot.ie/
Twitter: https://twitter.com/guglielmoiozzia

Building a data pipeline to ingest data into Hadoop in minutes using Streamsets Data Collector

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1. Building a data pipeline to ingest data into Hadoop in minutes using Streamsets Data Collector Guglielmo Iozzia, Big Data Infrastructure Engineer @ IBM Ireland

2. Data Ingestion for Analytics: a real scenario In the business area (cloud applications) to which my team belongs there were so many questions to be answered. They were related to: ● Defect analysis ● Outage analysis ● Cyber-Security

3. “Data is the second most important thing in analytics”

4. Data Ingestion: multiple sources... ● Legacy systems ● DB2 ● Lotus Domino ● MongoDB ● Application logs ● System logs ● New Relic ● Jenkins pipelines ● Testing tools output ● RESTful Services

5. … and so many tools available to get the data

6. What are we going to do with all those data?

7. Issues ● The need to collect data from multiple sources introduces redundancy, which costs additional disk space and increases query times. ● A small team. ● Lack of skills and experience across the team (and the business area in general) in managing Big Data tools. ● Low budget.

8. Alternatives #1 Panic

9. Alternatives #2 Cloning team members

10. Alternatives #3 Find a smart way to simplify the data ingestion process

11. A single tool needed... ● Design complex data flows with minimal coding and the maximum flexibility. ● Provide real-time data flow statistics, metrics for each flow stage. ● Automated error handling and alerting. ● Easy to use by everyone. ● Zero-downtime when upgrading the infrastructure due to logical isolation of each flow stage. ● Open Source

12. … something like this

13. Streamsets Data Collector

14. Streamsets Data Collector

15. Streamsets Data Collector: supported origins

16. Streamsets Data Collector: available destinations

17. Streamsets Data Collector: available processors ● Base64 Field Decoder ● Base64 Field Encoder ● Expression Evaluator ● Field Converter ● JavaScript Evaluator ● JSON Parser ● Jython Evaluator ● Log Parser ● Stream Selector ● XML Parser ...and many others

18. Streamsets Data Collector Demo

19. Streamsets DC: performance and reliability ● Two available execution modes: standalone or cluster ● Implemented in Java: so any performance best practice/recommendation for Java applications applies here ● REST services for performance monitoring available ● Rules and alerts (metric and data both)

20. Streamsets Data Collector: security ● You can authenticate user accounts based on LDAP ● Authorization: the Data Collector provides several roles (admin, manager, creator, guest) ● You can use Kerberos authentication to connect to origin and destination systems ● Follow the usual security best practices in terms of iptables, networking, etc. for Java web applications running on Linux machines.

21. Useful Links Streamsets Data Collector: https://streamsets.com/product/

22. Thanks! My contacts: Linkedin: https://ie.linkedin.com/in/giozzia Blog: http://googlielmo.blogspot.ie/ Twitter: https://twitter.com/guglielmoiozzia

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