Author Archives: atiru

About atiru

Product Strategist and architect for harnessing value from data.

Key aspects for realizing smart data lakes

Data lakes is an ubiquitous term that is commonly used by vendors and enterprises.  Typically,  data lakes are spoken as a solution for data silos and as a enabler for digital transformation. The key idea behind the data lake is … Continue reading

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Platform Flexibility – Key for realizing IoT solutions

One of the realities of realizing an IoT solution (Enterprise, Commercial, Industrial) is that the requirements so drastically different.  Among the several dimensions that are used to evaluate a platform for realizing an IoT solution, a key dimension is the … Continue reading

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Introducing HSDP – A platform for realizing streaming analytics for an IoT solution

Since the last one year or so I have been involved in defining the platform vision, features and go to market strategy for HSDP – Hitachi’s Streaming Data Analytics Platform.  Streaming analytics is not something new, however, the range of … Continue reading

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Data Preparation – Normalization Subsystem – Clustering using Tokens

Continuing on the subject related to clustering text to facilitate normalizing a data set in this blog post I will examine clustering using tokens. Token based clustering uses  tokens to evaluate similarity between two string and determine membership into a cluster. The … Continue reading

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Data preparation – Normalization subsystem – Clustering Text using Distance Methods

Continuing from my previous blog (Data preparation – Normalization subsystem – Clustering Text using Fingerprinting) in this blog I will examine the distance approach a.k.a nearest neighbor  to clustering text strings. The distance approach to clustering provides better flexibility in finding … Continue reading

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Data preparation – Normalization subsystem – Clustering Text using Fingerprinting

In this blog I will examine the normalization sub-system which is one of the sub-systems I called in my earlier blog – Data Preparation Sub-Systems. A key objective of this step is to ensure the data consistency.  For example, when working … Continue reading

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Ensuring data consistency between cloud and on-premises

Enterprises today have greater flexibility in determining whether investing in applications, platforms and infrastructure should be a capital expenditure or operational expenditure or both.  As such enterprises are increasingly using a mix of public cloud, private cloud and on-premises strategy … Continue reading

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