Re-Shaping Data Flows

Maneesh Varshney explains some methods to trim the fat out of analytical data flows:

Big data comes in a variety of shapes. The Extract-Transform-Load (ETL) workflows are more or less stripe-shaped (left panel in the figure above) and produce an output of a similar size to the input. Reporting workflows are funnel-shaped (middle panel in the figure above) and progressively reduce the data size by filtering and aggregating.

However, a wide class of problems in analytics, relevance, and graph processing have a rather curious shape of widening in the middle before slimming down (right panel in the figure above). It gets worse before it gets better.

In this article, we take a deeper dive into this exploding middle shape: understanding why it happens, why it’s a problem, and what can we do about it. We share our experiences of real-life workflows from a spectrum of fields, including Analytics (A/B experimentation), Relevance (user-item feature scoring), and Graph (second degree network/friends-of-friends).

The examples relate directly to Hadoop, but are applicable in other data platforms as well.

Related Posts

Anomaly Detection With Kafka Streams

Ajmal Karuthakantakath shows us an application which performs fairly simple anomaly detection using Kafka Streams: The problem is in the banking loan payment domain, where customers have taken a loan and they need to make monthly payments to repay the loan amount. Assume there are millions of customers in the system and all these customers need […]

Read More

Crossing The Streams With Kafka

Himani Arora shows how to join two Kafka streams together: KStream-KStream Join It is a sliding window join, that means, all tuples close to each other with regard to time are joined. Time here is the difference up to size of the window. These joins are always windowed joins because otherwise, the size of the internal state […]

Read More

Categories

June 2017
MTWTFSS
« May Jul »
 1234
567891011
12131415161718
19202122232425
2627282930