146 Extremely Powerful Data Lake Architecture Questions You Do Not Know

What is involved in Data Lake Architecture

Find out what the related areas are that Data Lake Architecture connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Data Lake Architecture thinking-frame.

How far is your company on its Data Lake Architecture journey?

Take this short survey to gauge your organization’s progress toward Data Lake Architecture leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.

To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.

Start the Checklist

Below you will find a quick checklist designed to help you think about which Data Lake Architecture related domains to cover and 146 essential critical questions to check off in that domain.

The following domains are covered:

Data Lake Architecture, Data lake, Amazon.com, Amazon S3, Apache Hadoop, Azure Data Lake, Big data, Cambridge Semantics, Cazena, Cloudera, Data analytics, Data mart, Data reporting, Data visualization, Data warehouse, Google, Information silo, Machine learning, Microsoft, Pentaho, PricewaterhouseCoopers, Teradata, Zaloni:

Data Lake Architecture Critical Criteria:

Deliberate over Data Lake Architecture failures and be persistent.

– Do those selected for the Data Lake Architecture team have a good general understanding of what Data Lake Architecture is all about?

– Looking at hadoop big data in the rearview mirror, what would you have done differently after implementing a Data Lake?

– Do we need an enterprise data warehouse, a Data Lake, or both as part of our overall data architecture?

– Can the data be obtained at no cost, or is there a charge associated with access?

– What data is being licensed, and how or where is it being made available?

– How do we manage Data Lake Architecture Knowledge Management (KM)?

– What kinds of use are permitted/prohibited by the license?

– How strict to be with dimensional design?

– What are the values at the data points?

– Where are they commonly created?

– What is Regulatory Compliance ?

– What processes touched my data?

– How is this data represented?

– Why analysis inside a DBMS?

– Where is the data located?

– What Is Data Governance ?

– Where did it come from?

– What is geostatistics ?

– MapReduce: forgotten?

– What method to use ?

Data lake Critical Criteria:

Facilitate Data lake goals and mentor Data lake customer orientation.

– Looking at hadoop big data in the rearview mirror what would you have done differently after implementing a Data Lake?

– Meeting the challenge: are missed Data Lake Architecture opportunities costing us money?

– What sources do you use to gather information for a Data Lake Architecture study?

– Did it get exported, when, where how will it be used (organizational)?

– Can I connect this data to data I already have?

– Can we realistically store everything?

– Where did my data come from ?

– Is Big data different?

– How old is this data?

Amazon.com Critical Criteria:

Look at Amazon.com failures and perfect Amazon.com conflict management.

– Does Data Lake Architecture systematically track and analyze outcomes for accountability and quality improvement?

– What knowledge, skills and characteristics mark a good Data Lake Architecture project manager?

– Have you identified your Data Lake Architecture key performance indicators?

Amazon S3 Critical Criteria:

Start Amazon S3 goals and find out.

– what is the best design framework for Data Lake Architecture organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?

– What prevents me from making the changes I know will make me a more effective Data Lake Architecture leader?

– Do we monitor the Data Lake Architecture decisions made and fine tune them as they evolve?

Apache Hadoop Critical Criteria:

Confer re Apache Hadoop adoptions and drive action.

– What are your most important goals for the strategic Data Lake Architecture objectives?

– What will drive Data Lake Architecture change?

– Are there Data Lake Architecture problems defined?

Azure Data Lake Critical Criteria:

Think carefully about Azure Data Lake governance and observe effective Azure Data Lake.

– What are the disruptive Data Lake Architecture technologies that enable our organization to radically change our business processes?

– Is maximizing Data Lake Architecture protection the same as minimizing Data Lake Architecture loss?

Big data Critical Criteria:

Scrutinze Big data risks and devise Big data key steps.

– Think about the kind of project structure that would be appropriate for your Data Lake Architecture project. should it be formal and complex, or can it be less formal and relatively simple?

– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?

– Which departments in your organization are involved in using data technologies and data analytics?

– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?

– How can the benefits of Big Data collection and applications be measured?

– Does your organization have a strategy on big data or data analytics?

– When we plan and design, how well do we capture previous experience?

– Are our business activities mainly conducted in one country?

– Big Data: what is different from large databases?

– Even when we have a lot of data, do we understand it?

– What are our tools for big data analytics?

– What happens if/when no longer need cognitive input?

– How do we measure value of an analytic?

– What preprocessing do we need to do?

– So how are managers using big data?

– Who is collecting all this data?

– what is Different about Big Data?

– Does Big Data Really Need HPC?

– Find traffic bottlenecks ?

– How much data so far?

Cambridge Semantics Critical Criteria:

Co-operate on Cambridge Semantics tactics and get the big picture.

– Do several people in different organizational units assist with the Data Lake Architecture process?

– Is there any existing Data Lake Architecture governance structure?

Cazena Critical Criteria:

Think about Cazena planning and adjust implementation of Cazena.

– How do you determine the key elements that affect Data Lake Architecture workforce satisfaction? how are these elements determined for different workforce groups and segments?

– What are the success criteria that will indicate that Data Lake Architecture objectives have been met and the benefits delivered?

– What is our Data Lake Architecture Strategy?

Cloudera Critical Criteria:

Pay attention to Cloudera outcomes and ask questions.

– Who will be responsible for making the decisions to include or exclude requested changes once Data Lake Architecture is underway?

– Who needs to know about Data Lake Architecture ?

– What is Effective Data Lake Architecture?

Data analytics Critical Criteria:

See the value of Data analytics quality and explain and analyze the challenges of Data analytics.

– What are the potential areas of conflict that can arise between organizations IT and marketing functions around the deployment and use of business intelligence and data analytics software services and what is the best way to resolve them?

– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

– What are the particular research needs of your organization on big data analytics that you find essential to adequately handle your data assets?

– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?

– Social Data Analytics Are you integrating social into your business intelligence?

– Is Data Lake Architecture Realistic, or are you setting yourself up for failure?

– what is the difference between Data analytics and Business Analytics If Any?

– Which individuals, teams or departments will be involved in Data Lake Architecture?

Data mart Critical Criteria:

Accelerate Data mart issues and suggest using storytelling to create more compelling Data mart projects.

– What new services of functionality will be implemented next with Data Lake Architecture ?

– What is the purpose of data warehouses and data marts?

Data reporting Critical Criteria:

Deduce Data reporting goals and improve Data reporting service perception.

– What role does communication play in the success or failure of a Data Lake Architecture project?

– Who will be responsible for documenting the Data Lake Architecture requirements in detail?

– How can we improve Data Lake Architecture?

Data visualization Critical Criteria:

Learn from Data visualization decisions and gather practices for scaling Data visualization.

– What are the best places schools to study data visualization information design or information architecture?

– How likely is the current Data Lake Architecture plan to come in on schedule or on budget?

– Think of your Data Lake Architecture project. what are the main functions?

– How can skill-level changes improve Data Lake Architecture?

Data warehouse Critical Criteria:

Air ideas re Data warehouse visions and catalog Data warehouse activities.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data Lake Architecture services/products?

– What does a typical data warehouse and business intelligence organizational structure look like?

– Does big data threaten the traditional data warehouse business intelligence model stack?

– How important is Data Lake Architecture to the user organizations mission?

– Is data warehouseing necessary for our business intelligence service?

– Is Data Warehouseing necessary for a business intelligence service?

– What is the difference between a database and data warehouse?

– What are alternatives to building a data warehouse?

– Do we offer a good introduction to data warehouse?

– Data Warehouse versus Data Lake (Data Swamp)?

– Do you still need a data warehouse?

– Centralized data warehouse?

Google Critical Criteria:

Review Google goals and assess and formulate effective operational and Google strategies.

– We keep record of data and store them in cloud services; for example Google Suite. There are data protection tools provided and security rules can be set. But who has the responsibility for securing them – us or Google?

– How does our CRM collaboration software integrate well with Google services like Google Apps and Google Docs?

– What other jobs or tasks affect the performance of the steps in the Data Lake Architecture process?

– Is Data Lake Architecture dependent on the successful delivery of a current project?

– Are we Assessing Data Lake Architecture and Risk?

Information silo Critical Criteria:

Communicate about Information silo outcomes and improve Information silo service perception.

– How do we know that any Data Lake Architecture analysis is complete and comprehensive?

– Who sets the Data Lake Architecture standards?

Machine learning Critical Criteria:

Graph Machine learning results and question.

– How can you negotiate Data Lake Architecture successfully with a stubborn boss, an irate client, or a deceitful coworker?

– Are assumptions made in Data Lake Architecture stated explicitly?

Microsoft Critical Criteria:

Confer over Microsoft leadership and explain and analyze the challenges of Microsoft.

– How can we truly understand and predict our customers needs to the point where we can design products and services that suit their needs?

– Why would potential clients outsource their business to us if they can perform the same level of Customer Service in house?

– Is a significant amount of your time taken up communicating with existing clients to resolve issues they are having?

– Does the current system allow for service cases to be opened in the CRM directly from the exchange site?

– It is often said that CRMs complexity is due to its quantity of functions. How do we handle this?

– Do you have a mechanism in place to quickly respond to visitor/customer inquiries and orders?

– What level of customer involvement is required during the implementation?

– How are we handling the risk of garbage in and garbage out with e-CRM?

– What are the roles of suppliers and supply chain partners in CRM?

– What is the network quality, including speed and dropped packets?

– How does Total Quality Service Effects Toward Customer Loyalty?

– How to you ensure compliance with client legal requirements?

– What are the fastest growing CRM solutions right now?

– Are the offline synchronization subscriptions valid?

– Does the current CRM contain escalation tracking?

– Are we better off going outside?

– What happens to customizations?

– Is there a known outage?

– Why is crm important?

Pentaho Critical Criteria:

Facilitate Pentaho tactics and arbitrate Pentaho techniques that enhance teamwork and productivity.

– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data Lake Architecture processes?

– Which OpenSource ETL tool is easier to use more agile Pentaho Kettle Jitterbit Talend Clover Jasper Rhino?

PricewaterhouseCoopers Critical Criteria:

Prioritize PricewaterhouseCoopers visions and give examples utilizing a core of simple PricewaterhouseCoopers skills.

– What are our best practices for minimizing Data Lake Architecture project risk, while demonstrating incremental value and quick wins throughout the Data Lake Architecture project lifecycle?

– What are current Data Lake Architecture Paradigms?

– Is the scope of Data Lake Architecture defined?

Teradata Critical Criteria:

Study Teradata tactics and reinforce and communicate particularly sensitive Teradata decisions.

– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Data Lake Architecture process. ask yourself: are the records needed as inputs to the Data Lake Architecture process available?

– Who is the main stakeholder, with ultimate responsibility for driving Data Lake Architecture forward?

– How do we Lead with Data Lake Architecture in Mind?

Zaloni Critical Criteria:

Pay attention to Zaloni decisions and find the essential reading for Zaloni researchers.

– Can we do Data Lake Architecture without complex (expensive) analysis?

– What business benefits will Data Lake Architecture goals deliver if achieved?

– Do we all define Data Lake Architecture in the same way?


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Data Lake Architecture Self Assessment:


Author: Gerard Blokdijk

CEO at The Art of Service | http://theartofservice.com



Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Data lake External links:

SMG Data Lake

How to Design a Successful Data Lake – Knowledgent

Data Lake | Microsoft Azure

Amazon.com External links:

Amazon.com: Interesting Finds

Amazon.com: Paintings

Amazon S3 External links:

Amazon S3 – Official Site

Amazon S3 Reviews | G2 Crowd

Amazon S3 HTML Title – Stack Overflow

Apache Hadoop External links:

Apache Hadoop | IBM Analytics

Apache Spark and Apache Hadoop on Google Cloud …

Apache Hadoop open source ecosystem | Cloudera

Azure Data Lake External links:

What Is Azure Data Lake? – Developer.com

Azure Data Lake Tools for Visual Studio 2015 – microsoft.com

Big data External links:

Business Intelligence and Big Data Analytics Software

Databricks – Making Big Data Simple

ZestFinance.com: Machine Learning & Big Data Underwriting

Cambridge Semantics External links:

cambridgesemantics.com – Cambridge Semantics

Training – Cambridge Semantics

The Smart Data Company® | Cambridge Semantics

Cazena External links:

Fully Managed Big Data Platform as a Service | Cazena

Cloudera External links:

CLDR Stock Quote – Cloudera, Inc. Common Stock Price – …

Cloudera – Official Site

Cloudera (@cloudera) | Twitter

Data analytics External links:

Twitter Data Analytics – TweetTracker

What is Data Analytics? – Definition from Techopedia

What is data analytics (DA)? – Definition from WhatIs.com

Data mart External links:

[PDF]Institutional Research Data Mart: Instructor Guide …

UNC Data Mart – University of North Carolina

MPR Data Mart

Data reporting External links:

Data Reporting Analyst Jobs, Employment | Indeed.com

Product Data Reporting and Evaluation Program

Grant Applications and Data Reporting

Data visualization External links:

geothinQ – On-demand Land Mapping & Data Visualization

What is data visualization? – Definition from WhatIs.com

Data Visualization | FEMA.gov

Data warehouse External links:

[PDF]Data Warehouse – Utility’s Smart Grid Clearinghouse
http://smartgrid.epri.com/UseCases/DW – Utility DOE SG Clearhouse_ph2add.pdf

Data Warehouse Specialist Salaries – Salary.com

EZ Data Warehouse

Google External links:

google-site-verification: googlef4aab024e2491460.html

Google (@Google) | Twitter


Information silo External links:

Information Silo – investopedia.com

Information silo – Revolvy
https://www.revolvy.com/topic/Information silo&item_type=topic

Jan 29, 2003 · Definition An information silo , or a group of such silos, is an insular management system in which one information system or …
http://Wednesday 2/7 Midday Web Weather – kxii.com

Machine learning External links:

What is machine learning? – Definition from WhatIs.com

Machine Learning Mastery – Official Site

DataRobot – Automated Machine Learning for Predictive …

Microsoft External links:

Microsoft OneNote – Official Site

Microsoft Careers

Microsoft Office | Productivity Tools for Home & Office

Pentaho External links:

Pentaho User Console – Login

PricewaterhouseCoopers External links:

Applicants Can Sue PricewaterhouseCoopers for …

Teradata External links:

title | Teradata Downloads

Add title to view column – Teradata Community

title stack | Teradata Downloads

Zaloni External links:

Zaloni – Official Site

Zaloni Inc: Company Profile – Bloomberg

Zaloni Jobs | Glassdoor

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