Cloud Hadoop Deployment
Connecting to DBs in Owl Web
Migration and Promoting

Cluster Health Report

How much is your redundant data costing you?

Reclaim Gigabytes of Redundant Data

As data engineers, first we copy files into a landing zone, next we load the files into a staging area. After that we transform (ETL) the data into the final table. Soon that same data is copied to a lake for other groups to run analytics on. Eventually a group of analysts will need the data in another format and a data engineer will copy the data in a newly joined or transposed fashion. Sounds familiar?

The result is the same data or similar columns of the same data being copied many times. The answer: Buy more hardware... could be OR run an Owl health report and gain an understanding of how much data could be removed, reclaiming disk space and instantly seeing a return on investment after clicking the button.

Tabular breakdown of % fingerprint match

Sometimes its not as simple as comparing 2 tables from the same database. Owl allows a technical user to setup multiple DB connections before executing an owl health check.

import com.owl.common.Props
import com.owl.core.Owl
val c1 = new Connection()
c1.dataset = "silo.account"
c1.user = "user"
c1.password = "pass"
c1.query = "select id, networth, acc_name, acc_branch from silo.account limit 200000"
c1.url = "jdbc:mysql://"
val c2 = new Connection()
c2.dataset = "silo.user_account"
c2.user = "user"
c2.password = "pass"
c2.query = "SELECT acc_name, acc_branch, networth FROM silo.account limit 200000"
c2.url = "jdbc:mysql://"
val props = new Props()
props.dataset = "colMatchTest1"
props.runId = "2017-02-04"
props.connectionList = List(c1,c2).asJava
props.colMatchBatchSize = 2
props.colMatchDurationMins = 3
val matchDF = new Owl(props).colMatchDF

High level view of data overlap