Content and talk by Friso van Vollenhoven (GoDataDriven) Let's face it: data warehousing in the traditional sense has been tedious, lacking agility, and slow. Often designed and built around a static set of up front questions, they are basically a big database tailored to the application of populating dashboards. The fact that the volume of data that we need to deal with recently exploded by several order of magnitude isn't improving the situation. It's no surprise that we see a new class of data warehousing setups emerge, using big data technologies en NoSQL stores. A nice side effect is that these solutions are usually not only the domain of the BI crowd, but can also be developer friendly and allow development of more data driven apps. In this talk I will present about experiences using Hadoop and other tools from the Hadoop ecosystem, such as Hive, Pig and bare MapReduce, to handle data that grows tens of GBs per day. We create a system where data is captured, stored and made available to different users and use cases, ranging from end users that write SQL queries to software developers that access the underlying data to create data driven products. I will cover topics like ETL, querying, development and deployment and reporting; using a fully open source stack, of course.

1. GoDataDriven
PROUDLY PART OF THE XEBIA GROUP
@fzk
frisovanvollenhoven@godatadriven.com
Building a Big Data DWH
Friso van Vollenhoven
CTO
Data Warehousing on Hadoop

2. -- Wikipedia
“In computing, a data warehouse or
enterprise data warehouse (DW,
DWH, or EDW) is a database used
for reporting and data analysis.”

6. ETL

8. How to:
• Add a column to the facts table?
• Change the granularity of dates from day
to hour?
• Add a dimension based on some
aggregation of facts?

9. Schema’s are designed
with questions in mind.
Changing it requires to
redo the ETL.

10. Schema’s are designed
with questions in mind.
Changing it requires to
redo the ETL.
Push things to the facts
level.
Keep all source data
available all times.

12. And now?
• MPP databases?
• Faster / better / more SAN?
• (RAC?)

14. distributed storage
distributed processing
metadata + query engine

16. EXTRACT
TRANSFORM
LOAD

22. • No JVM startup overhead for Hadoop API usage
• Relatively concise syntax (Python)
• Mix Python standard library with any Java libs

24. • Flexible scheduling with dependencies
• Saves output
• E-mails on errors
• Scales to multiple nodes
• REST API
• Status monitor
• Integrates with version control

26. Deployment
git push jenkins master

27. •Scheduling
•Simple deployment of ETL code
•Scalable
•Developer friendly

33. 'februari-22 2013'

35. A: Yes, sometimes as
often as 1 in every 10K
calls. Or about once a
week at 3K files / day.

38. þ

39. þ

40. TSV ==
thorn separated values?

41. þ == 0xFE

42. or -2, in Hive
CREATE TABLE browsers (
browser_id STRING,
browser STRING
) ROW FORMAT DELIMITED FIELDS TERMINATED BY '-2';

49. • The format will change
• Faulty deliveries will occur
• Your parser will break
• Records will be mistakingly produced (over-logging)
• Other people test in production too (and you get the
data from it)
• Etc., etc.

50. •Simple deployment of ETL code
•Scheduling
•Scalable
•Independent jobs
•Fixable data store
•Incremental where possible
•Metrics

51. Independent jobs
source (external)
staging (HDFS)
hive-staging (HDFS)
Hive
HDFS upload + move in place
MapReduce + HDFS move
Hive map external table + SELECT INTO

52. Out of order jobs
• At any point, you don’t really know what ‘made it’
to Hive
• Will happen anyway, because some days the data
delivery is going to be three hours late
• Or you get half in the morning and the other half
later in the day
• It really depends on what you do with the data
• This is where metrics + fixable data store help...

53. Fixable data store
• Using Hive partitions
• Jobs that move data from staging create partitions
• When new data / insight about the data arrives,
drop the partition and re-insert
• Be careful to reset any metrics in this case
• Basically: instead of trying to make everything
transactional, repair afterwards
• Use metrics to determine whether data is fit for
purpose

54. Metrics

55. Metrics service
• Job ran, so may units processed, took so much
time
• e.g. 10GB imported, took 1 hr
• e.g. 60M records transformed, took 10 minutes
• Dropped partition
• Inserted X records into partition

56. GoDataDriven
We’re hiring / Questions? / Thank you!
@fzk
frisovanvollenhoven@godatadriven.com
Friso van Vollenhoven
CTO