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

Stream-To-Stream Joins In Spark

Ayush Tiwari shows how to join a pair of streams in Apache Spark 2.3: In Spark 2.3, it added support for stream-stream joins, i.e, we can join two streaming Datasets/DataFrames and in this blog we are going to see how beautifully spark now give support for joining the two streaming dataframes. I this example, I […]

Read More

Spark: DataFrame To RDD For Data Cleansing

Gilad Moscovitch walks us through a common data cleansing problem with Spark data frames: A problem can arise when one of the inner fields of the json, has undesired non-json values in some of the records. For instance, an inner field might contains HTTP errors, that would be interpreted as a string, rather than as a […]

Read More

Categories

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