data-driven decision making is at the center of all things. The emergence of
data science and machine learning has further reinforced the importance of data
as the most critical commodity in today’s world. From FAAMG (the biggest five
tech companies: Facebook, Amazon, Apple, Microsoft, and Google) to governments
and non-profits, everyone is busy leveraging the power of data to achieve final
goals. Unfortunately, this growing demand for data has exposed the inefficiency
of the current systems to support the ever-growing data needs. This
inefficiency is what led to the evolution of what we today know as Logical Data
What Is a Logical
simple words, a data lake is a data repository that is capable of storing any
data in its original format. As opposed to traditional data sources that
use the ETL (Extract, Transform, and Load) strategy, data lakes work on the ELT
(Extract, Load, and Transform) strategy. This means data does not have to be
first transformed and then loaded, which essentially translates into reduced
time and efforts. Logical data lakes have captured the attention of
millions as they do away with the need to integrate data from different data
repositories. Thus, with this open access to data, companies can now begin to
draw correlations between separate data entities and use this exercise to their
Primary Use Case
Scenarios of Data Lakes
Logical data lakes are a
relatively new concept, and thus, readers can benefit from some knowledge of
how logical data lakes can be used in real-life scenarios.
Experimental Analysis of Data:
Logical data lakes can
play an essential role in the experimental analysis of data to establish its
value. Since data lakes work on the ELT strategy, they grant deftness and speed
to processes during such experiments.
To store and
analyze IoT Data:
Logical data lakes can
efficiently store the Internet of Things type of data. Data lakes are capable
of storing both relational as well as non-relational data. Under logical data
lakes, it is not mandatory to define the structure or schema of the data
stored. Moreover, logical data lakes can run analytics on IoT data and come up
with ways to enhance quality and reduce operational cost.
To improve Customer
Logical data lakes can
methodically combine CRM data with social media analytics to give businesses an
understanding of customer behavior as well as customer churn and its various
To create a Data
Logical data lakes
contain raw data. Data warehouses, on the other hand, store structured and
filtered data. Creating a data lake is the first step in the process of data
warehouse creation. A data lake may also be used to augment a data warehouse.
reporting and analytical function:
Data lakes can also be
used to support the reporting and analytical function in organizations. By
storing maximum data in a single repository, logical data lakes make it easier
to analyze all data to come up with relevant and valuable findings.
A logical data lake is a comparatively new area of study. However, it can be said with certainty that logical data lakes will revolutionize the traditional data theories.
Infosphere Information Server (IIS) as a variety of component tools to perform different activities. Many of these component tools are share across organization boundaries based on Role-based access control (RBAC). However, for all the components in IIS, the metadata the capture in the repository are of three types (Business, Technical, Operational).
This metadata is intended for business user consumption and consists of: Business rules, Stewardship, Business Definitions, Auditing Terminology, Glossaries, Algorithms and Lineage using business language.
This metadata is intended for specific component Users – Cognos (business intelligence), DataStage & DataQuality (information integration), Information Analyzer (profiling), Data Architect (modeling) and consists of: Source and Target systems definitions, Data Models (logical and Physical), Table and Fields structures and attributes, Documentation for Auditing Derivations and Dependencies, and Cognos Semantics for reporting.
This metadata is intended for Operations, Management, and Business Users and consists of: Information about application runs: their frequency, record counts, component by component analysis and other statistics for auditing purposes.
In recent history, I have been asked several times to describe where different IIS components fit in the Software Development Lifecycle (SDLC) process. The graphic above, list most of the more important IIS components in their relative SDLC relationships. However, it is important to note that that these are not absolutes. Many applications may cross boundaries depending on the practices of the individual company, the application spurt licensed by the company, and/or the applications implemented by the company. For example, many components will participate in the sustainment phase of SDLC, although I did not list him in that role. This is especially true, if you’re using the governance tools (e.g. governance catalog ) and supporting your sustainment activities with modeling and development tools, such as, data architect.
Each IIS component has a primary function in the InfoSphere architecture, which can be synopsized as follows:
IBM InfoSphere Blueprint Director is aimed at the Information Architect designing solution architectures for information-intensive projects.
Cognos (If purchased)
Governance Dashboard (Framework Manager Model provided by IBM), Semantics, Analytics, and Reporting
Data Architect is an enterprise data modeling and integration design tool. You can use it to discover, model, visualize, relate, and standardize diverse and distributed data assets, including dimensional models.
Data Click is an exciting new capability that helps novices and business users retrieve data and provision systems easily in only a few clicks.
DataStage is a data integration tool that enables users to move and transform data between operational, transactional, and analytical target systems.
Discovery is used to identify the transformation rules that have been applied to source system data to populate a target. Once accurately defined, these business objects and transformation rules provide the essential input into information-centric projects.
FastTrack streamlines collaboration between business analysts, data modelers, and developers by capturing and defining business requirements in a common format and, then, transforming that business logic (Source-to-Target-Mapping (STTM)) directly into DataStage ETL jobs.
Business Glossary Anywhere, its companion module, augments Governance Catalog with more ease-of-use and extensibility features.
The Governance Catalog includes business glossary assets (categories, terms, information governance policies, and information governance rules) and information assets.
Information Analyzer provides capabilities to profile and analyze data.
Information Services Director
Information Services Director provides a unified and consistent way to publish and manage shared information services in a service-oriented architecture (SOA).
Metadata Asset Manager
Import, export, and manage common metadata assets in Metadata Repository and across applications
Admin workspaces to investigate data, deploy applications, Web services, and monitor schedules and logs.
QualityStage provides data cleansing capabilities to help ensure quality and consistency by standardizing, validating, matching, and merging information to create comprehensive and authoritative information.
Deployment tool to move, deploy and control DataStage and QualityStage assets.