The Definitive Guide to MongoDB Analytics
|
|
- Camron Tate
- 6 years ago
- Views:
Transcription
1 The Definitive Guide to MongoDB Analytics
2 Analytics on MongoDB is a different beast than what you re familiar with. Don t expect to fire up your existing analytics tool, point it at MongoDB, and go. This guide explains why a fundamentally different approach is necessary and what your options are.
3 A Quick History Lesson This is where you probably are, and what led you to reading this article. The Question How do you use existing solutions to analyze live, deeply nested, semi-structured, schema-free data in MongoDB? Everything Has Failed How do you use existing solutions to analyze live, deeply nested, semistructured, schema-free data in MongoDB? The Answer You don t. It s not possible. At all. What is needed is a way to analyze this new data format in a way that is both obvious to use but also natively understands the nested, schema-less data. Even when it changes. On the fly. No tools that exist for relational databases can do this. Not one. So then, what are the options?
4 Enter MongoDB+ Enter MongoDB in the late 2000 s. Terms like schema free, semistructured data and JSON become the norm while Entity Relationship Diagrams and DDLs become less prominent. The Three Vs defined by Gartner (Volume, Variety and Velocity) are starting to define the purpose of NoSQL vendors compared to their older relational cousins. New Databases, Great Improvements, But MongoDB and the new generation of databases brings with them great improvements but also require a new approach to analytics. MongoDB and the new generation of databases brings with them great improvements: Improved developer productivity Massive scalability Unheard of reliability Great performance by any standard Not to mention they re typically open source and, when compared to big-name vendors, can be had for pennies on the dollar for production support. But with these advancements comes a new way of thinking about your data.
5 Data Models? What Data Models?...No actual schema does not imply that the developer or DBA doesn t need to coordinate on the best approach... MongoDB requires no actual schema but that does not imply the developer and the DBA don t need to coordinate on the best approach to a long term solution. The advantages of NoSQL databases are often simply the result of saving and accessing data in new ways. For MongoDB that means storing complete objects in one compact area of disk (or memory, or CPU cache, or ). From a developer s perspective, however the object looks in memory is exactly how it looks stored in MongoDB. It s physically stored as a single document in a binary form of JSON referred to as BSON. Why? Consider this: it takes much less time and effort to load a truck full of parts from a single store and deliver it to a customer, than to have a truck (or multiple trucks) pick up parts from multiple stores before delivery to the same customer. The same goes for anything in our physical reality: it takes real energy and real clock cycles to perform X actions. If everything is kept in a single location there is only 1 action to perform, versus X multiplied by the number of relational tables the object is stored in. Many NoSQL databases, especially MongoDB, prefer to store data as complete objects in one location rather than discrete, normalized, small bits of information in a dozen or even a hundred different locations (tables). This is often the most difficult aspect to accept and understand when moving from relational databases to MongoDB. Remember: Not only is data stored in the same location, it is stored in a completely different format. Making the transition to MongoDB often finds developers and DBAs asking questions such as: How do I join this data? How do I sub-select? What key do I use?
6 Data Models? What Data Models? (cont d.) How do I create a 1/N/M to 1/N/M relationship here? How can one document (row) be completely different from the next? Why can t I get a standard schema description? Those questions aren t as important, and sometimes don t apply anymore, because remember: all the data for a single object can be stored together. Check out at the example on the next page. Say there is a database with two tables that store information about books and authors. In a relational database it would be easy to setup: Simple creation. Simple key relationship. This could work for a small or medium-sized data set without much change. It supports 1:1 (one-to-one) and 1:M (one-to-many) relationships. Adding a relationship table could add support for M:M (many-to-many) relationships. In JSON (MongoDB s document format of choice), the following data structure could be used:
7 Data Models? What Data Models? (cont d.) Retrieving this author document from the database also fetches the books the author wrote because those are also part of the document. This is great because it saves time by reading all the related data in a single action. But what about the flip side: what if the user wanted to find out more about the author based on a book instead? This is an important thing to note, and something MongoDB teaches their employees: the data model should be created based on expected query patterns from applications, not what the developer finds most convenient. This is the only way to ensure performance at scale.
8 Data Models? What Data Models? (cont d.) As an example: assume a user searches for all books with an ISBN starting with While the data model above lends itself well to finding an author and his or her books, and makes sense in a developer s object-oriented brain, it won t perform well searching for books by ISBN. Why? Because MongoDB has to look through each author document, then look at each array inside of that document to know whether it matches the user s query. An index could be added to the books array, which might help, but now adds another index MongoDB needs to write to when adding, updating or deleting data. Also be careful about adding indexes to arrays with MongoDB it can be a real problem down the road. Another option is to store book information in a separate collection and then refer to it in a second query or with a $lookup MongoDB aggregation command. Unfortunately that approach negates the performance benefits of avoiding JOINs originally. This is why the data model must be designed well in advance. MongoDB requires no actual schema but that does not imply the developer and the DBA don t need to coordinate on the best approach to a long term solution.
9 The Big Question: What Do I Do For Analytics on NoSQL? This is where you probably are, and what led you to reading this article. The Question How do you use existing solutions to analyze live, deeply nested, semi-structured, schema-free data in MongoDB? Everything Has Failed How do you use existing solutions to analyze live, deeply nested, semistructured, schema-free data in MongoDB? The Answer You don t. It s not possible. At all. What is needed is a way to analyze this new data format in a way that is both obvious to use but also natively understands the nested, schema-less data. Even when it changes. On the fly. No tools that exist for relational databases can do this. Not one. So then, what are the options?
10 Option #1: Custom Coding You ve built a really cool app using MongoDB. That s awesome! MongoDB provides drivers for every major programming language with solid documentation. They ve lowered the barrier of entry for developers to get started which is great. Getting simple data out of MongoDB isn t terribly hard. Usually. Getting meaningful data for making business decisions can be a different story. Many readers will relate to this it s a major reason this article was written. Developers can write apps quickly and utilize the JSON model to rapidly prototype ideas. It s easy to shove data into MongoDB. Getting simple data out isn t terribly hard, usually. Getting meaningful data for making business decisions can be a different story. Many readers will relate to this it s a major reason this article was written. You owe it to yourself to read this article by our CTO, John De Goes. It discusses the difficulties around creating dashboards geared toward customers and decision makers, all while avoiding the money pit that comes with custom-coded reporting. Pros Maximum flexibility Cons Everything is custom Significant MongoDB knowledge required Long term support of the custom solution Significant investment of time One-off solution for MongoDB Consider this approach when you: Have employees on the bench Already have deep knowledge of MongoDB aggregation and mapreduce functions Understand third party visualization tool integration
11 Option #2: ETL If you must use an existing two-dimensional (relational) database reporting tool with your multi-dimensional (nonrelational) database, this is the only option. There is simply no way to get a relational reporting tool to read, understand or display MongoDB data. Old Tools Don t Adapt. There is simply no way to get a relational reporting tool to read, understand or display MongoDB data. The Extract-Transform-Load approach has been used for a very long time with relational databases. I won t go into the details since there are better (and longer) articles elsewhere, and you ve likely investigated this route already. I will, however, give an example of why this approach is very difficult to implement from the technical perspective. Many people use MongoDB s ability to leverage the schema itself as data. What does this mean? It means you can use the field name (or column name in relational terms) as part of the data. Take the following JSON document as an example:
12 Option #2: ETL (cont d.) Notice the field names 2016-Q1 and 2016-Q2? We didn t list the field name as quarter and the field value as 2016-Q1. The field name is the value, and the remainder of the field value contains even more information. An ETL process takes rich, nested and self-described data and forces it into small, rigidly-typed containers so it can report in a confined, rigidly-typed way. Data fidelity will be lost during conversion with this kind of data. When considering an ETL approach readers must consider these points: How will deeply nested arrays be mapped to twodimensional tables? How will documents in the same collection, but with different schemas, be mapped? How will MongoDB schema changes be handled? Can the ETL solution handle the volume, variety and velocity of data? Will the solution scale to include new MongoDB applications and data? How fresh is the data from the ETL process? Bring your attention back to the first bullet point. Someone will need to manually map the MongoDB data model to the relational ETL model. This will need to happen again whenever the schema changes, and with MongoDB, the schema can change frequently. While scripts can be written, and some very basic tools exist that can handle the most rudimentary parts of this, the fact is that the vast majority of the data model can only be mapped by a human. Again, see the example document above.
13 Option #2: ETL (cont d.) Pros Most two-dimensional reporting tools can read the transformed data model. Cons Weeks or months of work to set up Cannot adjust to documents with different schemas Part-time or full-time DBA to maintain Significant investment in hardware, employee time and process Loss of data fidelity Accept that analysis is not live Consider this approach when you: Have already heavily invested in legacy reporting tools Are expected to use existing tools Can accept loss of data fidelity
14 Option #3: Native NoSQL Analytics with SlamData Native NoSQL analytics is a completely different approach to analytics. Given the document models shown earlier it s easy to see the disconnect that legacy relational reporting tools have. A Modern Tool for Modern Data SlamData is comprised of two primary pieces: the SlamData web application and the Quasar analytics engine. Both were designed from the beginning, in tandem, to understand and interact with NoSQL data like MongoDB. Any solution designed for multidimensional, NoSQL analytics must be designed and built for this from the ground up. Once a solution for multi-dimensional data is developed it s possible to then go back and apply it to two-dimensional data. It doesn t work the other way around. Unfortunately for existing BI vendors, the ability to natively work with MongoDB is not something that can be bolted on or included in a new version of an existing product. The best they can hope for is to mimic the ETL option. Make no mistake: reporting tools designed for relational databases will not analyze live, nested MongoDB data. Even MongoDB s official BI Connector performs an ETL process and stores data in PostgreSQL for two-dimensional analytics. SlamData is comprised of two primary pieces: the SlamData web application and the Quasar analytics engine. Both were designed from the beginning, in tandem, to understand and interact with NoSQL data like MongoDB. The SlamData product is a single analytics solution for business analysts, data scientists, developers, data architects and DBAs working with MongoDB. It natively understands dynamic, nested data and provides an interface built for it. All actions performed by SlamData occur on live data. Commands are sent to MongoDB in the most performant order based on the user s requested search. MongoDB
15 Option #3: Native NoSQL Analytics with SlamData Native NoSQL analytics is a completely different approach to analytics. Given the document models shown earlier it s easy to see the disconnect that legacy relational reporting tools have. A Modern Tool for Modern Data SlamData is comprised of two primary pieces: the SlamData web application and the Quasar analytics engine. Both were designed from the beginning, in tandem, to understand and interact with NoSQL data like MongoDB. Any solution designed for multidimensional, NoSQL analytics must be designed and built for this from the ground up. Once a solution for multi-dimensional data is developed it s possible to then go back and apply it to two-dimensional data. It doesn t work the other way around. Unfortunately for existing BI vendors, the ability to natively work with MongoDB is not something that can be bolted on or included in a new version of an existing product. The best they can hope for is to mimic the ETL option. Make no mistake: reporting tools designed for relational databases will not analyze live, nested MongoDB data. Even MongoDB s official BI Connector performs an ETL process and stores data in PostgreSQL for two-dimensional analytics. SlamData is comprised of two primary pieces: the SlamData web application and the Quasar analytics engine. Both were designed from the beginning, in tandem, to understand and interact with NoSQL data like MongoDB. The SlamData product is a single analytics solution for business analysts, data scientists, developers, data architects and DBAs working with MongoDB. It natively understands dynamic, nested data and provides an interface built for it. All actions performed by SlamData occur on live data. Commands are sent to MongoDB in the most performant order based on the user s requested search. MongoDB
16 Option #3: Native NoSQL Analytics with SlamData (cont d.) performs 100% of the computation and only the results are returned to SlamData. This is a key difference to understand: with ETL and existing BI tools an entire table (typically many tables) are returned and the solution must then perform analytics on the entire data set. A Modern Tool for Modern Data SlamData is comprised of two primary pieces: the SlamData web application and the Quasar analytics engine. Both were designed from the beginning, in tandem, to understand and interact with NoSQL data like MongoDB. With SlamData custom analytical workflows can be created by adding discrete action cards on top of one another. This allows actions such as querying MongoDB with SQL, displaying tabular reports, graphical charts, interactive forms and more. Cards can be stacked based on whatever the user is trying to accomplish. Developers can dynamically pass values into workspaces to control content and flow. DBAs can easily view schema and data. Business analysts can use standard SQL queries against MongoDB nested data. Users can interact with forms that allow self-service. All workflows, or any part of a workflow, can be securely embedded into other applications.
17 Option #3: Native NoSQL Analytics with SlamData (cont d.) Users can install SlamData and create dashboards on live MongoDB data in less than 60 minutes, regardless of schema. SlamData runs on Linux, OS X and Windows. It can also run on laptops, workstations, or as a server. It can run on bare metal or virtualvized. Since SlamData pushes 100% of its queries directly to the database for processing, there s no need for massive data transfers or heavy system requirements. The more optimized a MongoDB architecture is, the better SlamData runs. SlamData connects to any MongoDB database including remote instances and SSL-encrypted deployments too. Pros Immediate ROI
18 Option #3: Native NoSQL Analytics with SlamData (cont d.) Create embeddable reports in minutes after install Natively view, analyze and display deeply nested, semistructured data Use an enhanced SQL dialect that works on both relational and NoSQL data, instead of learning MongoDB s multiple proprietary approaches Graphically layout interactive forms, reports and charts Provide Google-like search functionality to MongoDB for end users Export data in multiple formats for custom processing Restrict data visibility and actions based on user authorization model Enterprise-grade multi-tenant security Cons Learning a new BI tool and new approach to MongoDB analytics Explaining the importance of this approach to nontechnical management Not as flexible as custom coding
19 There Is No Magic; It s Algebra And It s Open Source Our co-founders are sometimes heard saying something like There is no magic to SlamData, it s all there for the world to see. That s both true and a little misleading. A Completely New Technology. 100% Open Source. 100% Scalable. It takes an engineering team skilled in database technologies, mathematics, analytics, and advanced software development patterns to create a long-term, comprehensive solution. This type of solution isn t written overnight, or in a few months. It takes years to fundamentally change the way multidimensional data is modeled, understood and presented. It takes an engineering team skilled in database technologies, mathematics, analytics, and advanced software development patterns to create a long-term, comprehensive solution. So while our code can be checked out and modified on GitHub, it doesn t mean every developer who clones it will understand it. It may, in fact, look like magic. Arthur C. Clarke s Third Law comes to mind here. It s advanced. Don t believe me? Check it out for yourself.
20 What The Market Wants, What The Market Needs We re good at MongoDB Analytics. Really good. But we re not stopping there. We haven t spent years developing this to provide an amazing solution for just MongoDB. SlamData is the sole company that has built technology that bridges all of your data. All data sources, one analytics solution. Picture this for a moment: All data sources, one analytics solution. Picture this for a moment: All data sources, one analytics solution. Relational databases? Check. NoSQL databases? Check. XML, JSON and other nested flat file formats? Check. Cross-datasource (federated) queries and joins? Check.
21 What The Market Wants, What The Market Needs Relational databases? Check. NoSQL databases? Check. XML, JSON and other nested flat file formats? Check. Cross-datasource (federated) queries and joins? Check. Query and display log data, relational data and NoSQL data at the same time? Check. Pivot Tables and multidimensional data structure viewers? Check. Open Source? Check. One platform to query and analyze all data sources in your company? Check. While I m writing this we have a team of engineers writing connectors for several other databases that will be included in SlamData version 3.1. With our QScript connector technology we can create a connector for any data source (database, file, API) in a matter of weeks. You can expect several new data sources to be supported in each major release of SlamData. All with the same functionality that we currently provide for MongoDB. With it s ability to use standard SQL² across various data sources simultaneously, out-of-the-box visualizations, one-click embedding, customizable analytics workflows, enterprisegrade security, multi-tenant hosting capabilities, virtual views, interactive forms, 100% in-database query execution, and more SlamData is the only sensible approach for database analytics today.
22 2016 Slamdata
Introduction to K2View Fabric
Introduction to K2View Fabric 1 Introduction to K2View Fabric Overview In every industry, the amount of data being created and consumed on a daily basis is growing exponentially. Enterprises are struggling
More informationQLIKVIEW ARCHITECTURAL OVERVIEW
QLIKVIEW ARCHITECTURAL OVERVIEW A QlikView Technology White Paper Published: October, 2010 qlikview.com Table of Contents Making Sense of the QlikView Platform 3 Most BI Software Is Built on Old Technology
More informationHow to analyze JSON with SQL
How to analyze JSON with SQL SCHEMA-ON-READ MADE EASY Author: Kent Graziano 1 What s inside 3 Semi-structured brings new insights to business 4 Schema? No need! 5 How Snowflake solved this problem 6 Enough
More informationBUYING DECISION CRITERIA WHEN DEVELOPING IOT SENSORS
BUYING DECISION CRITERIA WHEN DEVELOPING IOT SENSORS PHILIP POULIDIS VIDEO TRANSCRIPT What is your name and what do you do? My name is Philip Poulidis and I m the VP and General Manager of Mobile and Internet
More informationOne of the fundamental kinds of websites that SharePoint 2010 allows
Chapter 1 Getting to Know Your Team Site In This Chapter Requesting a new team site and opening it in the browser Participating in a team site Changing your team site s home page One of the fundamental
More informationHow to integrate data into Tableau
1 How to integrate data into Tableau a comparison of 3 approaches: ETL, Tableau self-service and WHITE PAPER WHITE PAPER 2 data How to integrate data into Tableau a comparison of 3 es: ETL, Tableau self-service
More informationShine a Light on Dark Data with Vertica Flex Tables
White Paper Analytics and Big Data Shine a Light on Dark Data with Vertica Flex Tables Hidden within the dark recesses of your enterprise lurks dark data, information that exists but is forgotten, unused,
More informationCrash Course in Modernization. A whitepaper from mrc
Crash Course in Modernization A whitepaper from mrc Introduction Modernization is a confusing subject for one main reason: It isn t the same across the board. Different vendors sell different forms of
More informationStages of Data Processing
Data processing can be understood as the conversion of raw data into a meaningful and desired form. Basically, producing information that can be understood by the end user. So then, the question arises,
More informationOracle Big Data Connectors
Oracle Big Data Connectors Oracle Big Data Connectors is a software suite that integrates processing in Apache Hadoop distributions with operations in Oracle Database. It enables the use of Hadoop to process
More informationIntroduction to User Stories. CSCI 5828: Foundations of Software Engineering Lecture 05 09/09/2014
Introduction to User Stories CSCI 5828: Foundations of Software Engineering Lecture 05 09/09/2014 1 Goals Present an introduction to the topic of user stories concepts and terminology benefits and limitations
More informationFIVE BEST PRACTICES FOR ENSURING A SUCCESSFUL SQL SERVER MIGRATION
FIVE BEST PRACTICES FOR ENSURING A SUCCESSFUL SQL SERVER MIGRATION The process of planning and executing SQL Server migrations can be complex and risk-prone. This is a case where the right approach and
More informationDesigning High-Performance Data Structures for MongoDB
Designing High-Performance Data Structures for MongoDB The NoSQL Data Modeling Imperative Danny Sandwell, Product Marketing, erwin, Inc. Leigh Weston, Product Manager, erwin, Inc. Learn More at erwin.com
More informationJAVASCRIPT CHARTING. Scaling for the Enterprise with Metric Insights Copyright Metric insights, Inc.
JAVASCRIPT CHARTING Scaling for the Enterprise with Metric Insights 2013 Copyright Metric insights, Inc. A REVOLUTION IS HAPPENING... 3! Challenges... 3! Borrowing From The Enterprise BI Stack... 4! Visualization
More informationTHINGS YOU NEED TO KNOW ABOUT USER DOCUMENTATION DOCUMENTATION BEST PRACTICES
5 THINGS YOU NEED TO KNOW ABOUT USER DOCUMENTATION DOCUMENTATION BEST PRACTICES THIS E-BOOK IS DIVIDED INTO 5 PARTS: 1. WHY YOU NEED TO KNOW YOUR READER 2. A USER MANUAL OR A USER GUIDE WHAT S THE DIFFERENCE?
More informationExcel Basics: Working with Spreadsheets
Excel Basics: Working with Spreadsheets E 890 / 1 Unravel the Mysteries of Cells, Rows, Ranges, Formulas and More Spreadsheets are all about numbers: they help us keep track of figures and make calculations.
More informationWhitepaper. Solving Complex Hierarchical Data Integration Issues. What is Complex Data? Types of Data
Whitepaper Solving Complex Hierarchical Data Integration Issues What is Complex Data? Historically, data integration and warehousing has consisted of flat or structured data that typically comes from structured
More informationGPU Accelerated Data Processing Speed of Thought Analytics at Scale
GPU Accelerated Data Processing Speed of Thought Analytics at Scale The benefits of Brytlyt s GPU Accelerated Database Brytlyt is an ultra-high performance database that combines patent pending intellectual
More informationDesigning dashboards for performance. Reference deck
Designing dashboards for performance Reference deck Basic principles 1. Everything in moderation 2. If it isn t fast in database, it won t be fast in Tableau 3. If it isn t fast in desktop, it won t be
More informationCreating a target user and module
The Warehouse Builder contains a number of objects, which we can use in designing our data warehouse, that are either relational or dimensional. OWB currently supports designing a target schema only in
More informationClose Your File Template
In every sale there is always a scenario where I can t get someone to respond. No matter what I do. I can t get an answer from them. When people stop responding I use the Permission To. This is one of
More informationUp and Running Software The Development Process
Up and Running Software The Development Process Success Determination, Adaptative Processes, and a Baseline Approach About This Document: Thank you for requesting more information about Up and Running
More informationSOFTWARE DEFINED STORAGE VS. TRADITIONAL SAN AND NAS
WHITE PAPER SOFTWARE DEFINED STORAGE VS. TRADITIONAL SAN AND NAS This white paper describes, from a storage vendor perspective, the major differences between Software Defined Storage and traditional SAN
More informationPopular SIEM vs aisiem
Popular SIEM vs aisiem You cannot flip a page in any Cybersecurity magazine, or scroll through security blogging sites without a mention of Next Gen SIEM. You can understand why traditional SIEM vendors
More informationKnowledge Happens. We Don t Use Databases. Integrating Oracle and Hadoop. Be Very Afraid. Much more inside... Vol. 27, No. 1 FEBRUARY 2013 $15
Vol. 27, No. 1 FEBRUARY 2013 $15 Knowledge Happens Be Very Afraid An eye-opening interview with the CTO of McAfee. See page 4. We Don t Use Databases Dream of freedom from the RDBMS. See page 16. Integrating
More informationTHE RISE OF. The Disruptive Data Warehouse
THE RISE OF The Disruptive Data Warehouse CONTENTS What Is the Disruptive Data Warehouse? 1 Old School Query a single database The data warehouse is for business intelligence The data warehouse is based
More informationArchitectural challenges for building a low latency, scalable multi-tenant data warehouse
Architectural challenges for building a low latency, scalable multi-tenant data warehouse Mataprasad Agrawal Solutions Architect, Services CTO 2017 Persistent Systems Ltd. All rights reserved. Our analytics
More informationModern Data Warehouse The New Approach to Azure BI
Modern Data Warehouse The New Approach to Azure BI History On-Premise SQL Server Big Data Solutions Technical Barriers Modern Analytics Platform On-Premise SQL Server Big Data Solutions Modern Analytics
More informationTitle: Episode 11 - Walking through the Rapid Business Warehouse at TOMS Shoes (Duration: 18:10)
SAP HANA EFFECT Title: Episode 11 - Walking through the Rapid Business Warehouse at (Duration: 18:10) Publish Date: April 6, 2015 Description: Rita Lefler walks us through how has revolutionized their
More informationAn Introduction to Big Data Formats
Introduction to Big Data Formats 1 An Introduction to Big Data Formats Understanding Avro, Parquet, and ORC WHITE PAPER Introduction to Big Data Formats 2 TABLE OF TABLE OF CONTENTS CONTENTS INTRODUCTION
More informationA quick guide to... Split-Testing
A quick guide to... Split-Testing In this guide... Learn how you can get the best results from your email campaign by testing it first! Just create up to five messages, with different personalization techniques,
More informationDocument your findings about the legacy functions that will be transformed to
1 Required slide 2 Data conversion is a misnomer. This implies a simple mapping of data fields from one system to another. In reality, transitioning from one system to another requires a much broader understanding
More informationEvaluation Guide for ASP.NET Web CMS and Experience Platforms
Evaluation Guide for ASP.NET Web CMS and Experience Platforms CONTENTS Introduction....................... 1 4 Key Differences...2 Architecture:...2 Development Model...3 Content:...4 Database:...4 Bonus:
More informationFrom Single Purpose to Multi Purpose Data Lakes. Thomas Niewel Technical Sales Director DACH Denodo Technologies March, 2019
From Single Purpose to Multi Purpose Data Lakes Thomas Niewel Technical Sales Director DACH Denodo Technologies March, 2019 Agenda Data Lakes Multiple Purpose Data Lakes Customer Example Demo Takeaways
More informationMongoDB Schema Design for. David Murphy MongoDB Practice Manager - Percona
MongoDB Schema Design for the Click "Dynamic to edit Master Schema" title World style David Murphy MongoDB Practice Manager - Percona Who is this Person and What Does He Know? Former MongoDB Master Former
More informationINTRODUCTION. In this guide, I m going to walk you through the most effective strategies for growing an list in 2016.
- Bryan Harris - INTRODUCTION In this guide, I m going to walk you through the most effective strategies for growing an email list in 2016. A lot of things are taught online that, quite honestly, just
More informationThe QuickStudy Guide for Zoho CRM
The QuickStudy Guide for Zoho CRM Susan Clark Cornerstone Solutions Inc. Houston The QuickStudy Guide for Zoho CRM Using Zoho Everyday How Did Quick Get Included in the Book Name? Using This QuickStudy
More informationDigital Marketing Manager, Marketing Manager, Agency Owner. Bachelors in Marketing, Advertising, Communications, or equivalent experience
Persona name Amanda Industry, geographic or other segments B2B Roles Digital Marketing Manager, Marketing Manager, Agency Owner Reports to VP Marketing or Agency Owner Education Bachelors in Marketing,
More informationModule - 17 Lecture - 23 SQL and NoSQL systems. (Refer Slide Time: 00:04)
Introduction to Morden Application Development Dr. Gaurav Raina Prof. Tanmai Gopal Department of Computer Science and Engineering Indian Institute of Technology, Madras Module - 17 Lecture - 23 SQL and
More informationUnit 10 Databases. Computer Concepts Unit Contents. 10 Operational and Analytical Databases. 10 Section A: Database Basics
Unit 10 Databases Computer Concepts 2016 ENHANCED EDITION 10 Unit Contents Section A: Database Basics Section B: Database Tools Section C: Database Design Section D: SQL Section E: Big Data Unit 10: Databases
More informationHow to set up SQL Source Control The short guide for evaluators
GUIDE How to set up SQL Source Control The short guide for evaluators 1 Contents Introduction Team Foundation Server & Subversion setup Git setup Setup without a source control system Making your first
More informationWhat is Grails4Notes(TM)?
What is Grails4Notes(TM)? Justin Hill, CTO, Prominic.NET, Inc. Copyright (c) 2014. All rights reserved. Trademarks mentioned herein are the rights of their respective owners. About me and Prominic: Co-founder
More informationBuilding your own BMC Remedy AR System v7 Applications. Maruthi Dogiparthi
Building your own BMC Remedy AR System v7 Applications Maruthi Dogiparthi Agenda Introduction New Goodies Navigation, tree widgets Data Visualization Plug-in framework Development Guidelines Tools BMC
More informationProgress DataDirect For Business Intelligence And Analytics Vendors
Progress DataDirect For Business Intelligence And Analytics Vendors DATA SHEET FEATURES: Direction connection to a variety of SaaS and on-premises data sources via Progress DataDirect Hybrid Data Pipeline
More informationDistributed Databases: SQL vs NoSQL
Distributed Databases: SQL vs NoSQL Seda Unal, Yuchen Zheng April 23, 2017 1 Introduction Distributed databases have become increasingly popular in the era of big data because of their advantages over
More informationIBM POWER SYSTEMS: YOUR UNFAIR ADVANTAGE
IBM POWER SYSTEMS: YOUR UNFAIR ADVANTAGE Choosing IT infrastructure is a crucial decision, and the right choice will position your organization for success. IBM Power Systems provides an innovative platform
More informationWhat is version control? (discuss) Who has used version control? Favorite VCS? Uses of version control (read)
1 For the remainder of the class today, I want to introduce you to a topic we will spend one or two more classes discussing and that is source code control or version control. What is version control?
More informationIn this chapter, I m going to show you how to create a working
Codeless Database Programming In this chapter, I m going to show you how to create a working Visual Basic database program without writing a single line of code. I ll use the ADO Data Control and some
More informationProServeIT Corporation Century Ave. Mississauga, ON L5N 6A4 T: TF: F: W: ProServeIT.
1 Table of Contents POST #1... 3 Why Use a SharePoint Content Management System? A Quick Guide for Executives & Managers [Downloadable Infographic]... 3 POST #2... 5 Branding SharePoint 6 Ways to Brand
More informationContractors Guide to Search Engine Optimization
Contractors Guide to Search Engine Optimization CONTENTS What is Search Engine Optimization (SEO)? Why Do Businesses Need SEO (If They Want To Generate Business Online)? Which Search Engines Should You
More informationHEARTLAND DEVELOPER CONFERENCE 2017 APPLICATION DATA INTEGRATION WITH SQL SERVER INTEGRATION SERVICES
HEARTLAND DEVELOPER CONFERENCE 2017 APPLICATION DATA INTEGRATION WITH SQL SERVER INTEGRATION SERVICES SESSION ABSTRACT: APPLICATION DATA INTEGRATION WITH SQL SERVER INTEGRATION SERVICES What do you do
More informationDesigning a New. Data Dashboard. January Page 1
Designing a New Data Dashboard January 2018 Page 1 Acknowledgements This guide was created under JSI s Center for Health Information, Monitoring & Evaluation (CHIME) with efforts led by Allison Schlak
More informationRethinkDB. Niharika Vithala, Deepan Sekar, Aidan Pace, and Chang Xu
RethinkDB Niharika Vithala, Deepan Sekar, Aidan Pace, and Chang Xu Content Introduction System Features Data Model ReQL Applications Introduction Niharika Vithala What is a NoSQL Database Databases that
More informationComposite Software Data Virtualization The Five Most Popular Uses of Data Virtualization
Composite Software Data Virtualization The Five Most Popular Uses of Data Virtualization Composite Software, Inc. June 2011 TABLE OF CONTENTS INTRODUCTION... 3 DATA FEDERATION... 4 PROBLEM DATA CONSOLIDATION
More informationCOMMUNICATION PROTOCOLS
COMMUNICATION PROTOCOLS Index Chapter 1. Introduction Chapter 2. Software components message exchange JMS and Tibco Rendezvous Chapter 3. Communication over the Internet Simple Object Access Protocol (SOAP)
More informationSQL, Scaling, and What s Unique About PostgreSQL
SQL, Scaling, and What s Unique About PostgreSQL Ozgun Erdogan Citus Data XLDB May 2018 Punch Line 1. What is unique about PostgreSQL? The extension APIs 2. PostgreSQL extensions are a game changer for
More informationTeiid Designer User Guide 7.5.0
Teiid Designer User Guide 1 7.5.0 1. Introduction... 1 1.1. What is Teiid Designer?... 1 1.2. Why Use Teiid Designer?... 2 1.3. Metadata Overview... 2 1.3.1. What is Metadata... 2 1.3.2. Editing Metadata
More informationWhen, Where & Why to Use NoSQL?
When, Where & Why to Use NoSQL? 1 Big data is becoming a big challenge for enterprises. Many organizations have built environments for transactional data with Relational Database Management Systems (RDBMS),
More informationINTRODUCTION. Chris Claterbos, Vlamis Software Solutions, Inc. REVIEW OF ARCHITECTURE
BUILDING AN END TO END OLAP SOLUTION USING ORACLE BUSINESS INTELLIGENCE Chris Claterbos, Vlamis Software Solutions, Inc. claterbos@vlamis.com INTRODUCTION Using Oracle 10g R2 and Oracle Business Intelligence
More information7+ GRAPHICS LIBRARIES TO ENHANCE YOUR EMBEDDED ANALYTICS
7+ GRAPHICS LIBRARIES TO ENHANCE YOUR EMBEDDED ANALYTICS TABLE OF CONTENTS INTRODUCTION...3 FONT AWESOME...4 GOOGLE WEB FONTS...5 ADOBE COLOR WHEEL...6 WEB LANGUAGES...7 CSS FRAMEWORKS...8 JAVASCRIPT LIBRARIES...9
More informationVirtualization. Q&A with an industry leader. Virtualization is rapidly becoming a fact of life for agency executives,
Virtualization Q&A with an industry leader Virtualization is rapidly becoming a fact of life for agency executives, as the basis for data center consolidation and cloud computing and, increasingly, as
More informationThere And Back Again
There And Back Again Databases At Uber Evan Klitzke October 4, 2016 Outline Background MySQL To Postgres Connection Scalability Write Amplification/Replication Miscellaneous Other Things Databases at Uber
More informationThe Idiot s Guide to Quashing MicroServices. Hani Suleiman
The Idiot s Guide to Quashing MicroServices Hani Suleiman The Promised Land Welcome to Reality Logging HA/DR Monitoring Provisioning Security Debugging Enterprise frameworks Don t Panic WHOAMI I wrote
More information11G Chris Claterbos, Vlamis Software Solutions, Inc.
ACCELERATE YOUR ORACLE DW DW WITH OLAP 11 11G Chris Claterbos, Vlamis Software Solutions, Inc. claterbos@vlamis.com INTRODUCTION When building business intelligence applications data is important, but
More informationMicrosoft certified solutions associate
Microsoft certified solutions associate MCSA: BI Reporting This certification demonstrates your expertise in analyzing data with both Power BI and Excel. Exam 70-778/Course 20778 Analyzing and Visualizing
More informationProvide Real-Time Data To Financial Applications
Provide Real-Time Data To Financial Applications DATA SHEET Introduction Companies typically build numerous internal applications and complex APIs for enterprise data access. These APIs are often engineered
More informationSecurity Automation Best Practices
WHITEPAPER Security Automation Best Practices A guide to making your security team successful with automation TABLE OF CONTENTS Introduction 3 What Is Security Automation? 3 Security Automation: A Tough
More information2016 All Rights Reserved
2016 All Rights Reserved Table of Contents Chapter 1: The Truth About Safelists What is a Safelist Safelist myths busted Chapter 2: Getting Started What to look for before you join a Safelist Best Safelists
More informationGradeConnect.com. User Manual
GradeConnect.com User Manual Version 2.0 2003-2006, GradeConnect, Inc. Written by Bernie Salvaggio Edited by Charles Gallagher & Beth Giuliano Contents Teachers...5 Account Basics... 5 Register Your School
More informationConsiderations for Mobilizing your Lotus Notes Applications
Considerations for Mobilizing your Lotus Notes Applications John Kingsley Teamstudio Technical Director Why Mobilize? It all started with email. Not any one email in particular, just the fact that you
More informationDOWNLOAD PDF SQL SERVER 2005 FOR DEVELOPERS
Chapter 1 : SQL Server Upgrade Considerations for DBAs and Developers Microsoft SQL Server Compact Edition (SQL Server Compact Edition) is designed for developers who need light weight, in process relational
More informationBringing OpenStack to the Enterprise. An enterprise-class solution ensures you get the required performance, reliability, and security
Bringing OpenStack to the Enterprise An enterprise-class solution ensures you get the required performance, reliability, and security INTRODUCTION Organizations today frequently need to quickly get systems
More informationXcelerated Business Insights (xbi): Going beyond business intelligence to drive information value
KNOWLEDGENT INSIGHTS volume 1 no. 5 October 7, 2011 Xcelerated Business Insights (xbi): Going beyond business intelligence to drive information value Today s growing commercial, operational and regulatory
More informationSQL in the Hybrid World
SQL in the Hybrid World Tanel Poder a long time computer performance geek 1 Tanel Põder Intro: About me Oracle Database Performance geek (18+ years) Exadata Performance geek Linux Performance geek Hadoop
More informationOn Media And Change: Think of What We ve Accomplished. Remarks & reflections by Matt Turner, MarkLogic, CTO, Media & Publishing
On Media And Change: Think of What We ve Accomplished Remarks & reflections by Matt Turner, MarkLogic, CTO, Media & Publishing Recorded at Copyright Clearance Center, Danvers, Mass. For podcast release
More informationA detailed comparison of EasyMorph vs Tableau Prep
A detailed comparison of vs We at keep getting asked by our customers and partners: How is positioned versus?. Well, you asked, we answer! Short answer and are similar, but there are two important differences.
More informationReal-Time & Big Data GIS: Best Practices. Suzanne Foss Josh Joyner
Real-Time & Big Data GIS: Best Practices Suzanne Foss Josh Joyner ArcGIS Enterprise With Real-time Capabilities Desktop Apps APIs visualization ingestion dissemination & actuation analytics storage Agenda:
More informationTransforming IT: From Silos To Services
Transforming IT: From Silos To Services Chuck Hollis Global Marketing CTO EMC Corporation http://chucksblog.emc.com @chuckhollis IT is being transformed. Our world is changing fast New Technologies New
More informationQuick Reference Design Guide
Presentation is everything. At one time or another, you have probably heard the phrase a book is judged by its cover. That s still true and probably even more so today because we live in a very distracted,
More informationE-Guide WHAT WINDOWS 10 ADOPTION MEANS FOR IT
E-Guide WHAT WINDOWS 10 ADOPTION MEANS FOR IT E nterprise adoption of Windows 10 isn t likely to follow the same pattern as for Windows 7, and that s a good thing, writes columnist Brian Madden. And even
More informationIs SharePoint the. Andrew Chapman
Is SharePoint the Andrew Chapman Records management (RM) professionals have been challenged to manage electronic data for some time. Their efforts have tended to focus on unstructured data, such as documents,
More informationLecture 1: Overview
15-150 Lecture 1: Overview Lecture by Stefan Muller May 21, 2018 Welcome to 15-150! Today s lecture was an overview that showed the highlights of everything you re learning this semester, which also meant
More informationThe New USB-C Standard and How to Select a Matching Docking Station
The New USB-C Standard and How to Select a Matching Docking Station WHITE PAPER What is USB-C? USB-C, the latest USB standard, is attracting attention from all over the technology industry, as it carries
More informationOracle Enterprise Manager 12c Sybase ASE Database Plug-in
Oracle Enterprise Manager 12c Sybase ASE Database Plug-in May 2015 Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information purposes only,
More informationJANUARY Migrating standalone ArcGIS Server to ArcGIS Enterprise
JANUARY 2018 Migrating standalone ArcGIS Server to ArcGIS Enterprise Copyright 2018 Esri All rights reserved. Printed in the United States of America. The information contained in this document is the
More informationOracle Warehouse Builder 10g Release 2 Integrating Packaged Applications Data
Oracle Warehouse Builder 10g Release 2 Integrating Packaged Applications Data June 2006 Note: This document is for informational purposes. It is not a commitment to deliver any material, code, or functionality,
More informationFor Volunteers An Elvanto Guide
For Volunteers An Elvanto Guide www.elvanto.com Volunteers are what keep churches running! This guide is for volunteers who use Elvanto. If you re in charge of volunteers, why not check out our Volunteer
More informationUSE CASE. Collect CLOSED CASE FEEDBACK. Salesforce Workflow. Clicktools Deployment TWO DEPLOYMENT APPROACHES. The basic activity flow goes like this:
USE CASE Support clearly has a major impact on customer experience, which is why it s a starting point for many Clicktools implementations. This document outlines an example solution for a closed case/ticket
More informationIntroduction to Data Science
UNIT I INTRODUCTION TO DATA SCIENCE Syllabus Introduction of Data Science Basic Data Analytics using R R Graphical User Interfaces Data Import and Export Attribute and Data Types Descriptive Statistics
More informationBurning CDs in Windows XP
B 770 / 1 Make CD Burning a Breeze with Windows XP's Built-in Tools If your PC is equipped with a rewritable CD drive you ve almost certainly got some specialised software for copying files to CDs. If
More informationIntroduction to Federation Server
Introduction to Federation Server Alex Lee IBM Information Integration Solutions Manager of Technical Presales Asia Pacific 2006 IBM Corporation WebSphere Federation Server Federation overview Tooling
More informationWeek - 01 Lecture - 04 Downloading and installing Python
Programming, Data Structures and Algorithms in Python Prof. Madhavan Mukund Department of Computer Science and Engineering Indian Institute of Technology, Madras Week - 01 Lecture - 04 Downloading and
More informationData Virtualization Implementation Methodology and Best Practices
White Paper Data Virtualization Implementation Methodology and Best Practices INTRODUCTION Cisco s proven Data Virtualization Implementation Methodology and Best Practices is compiled from our successful
More informationThe Salesforce Migration Playbook
The Salesforce Migration Playbook By Capstorm Table of Contents Salesforce Migration Overview...1 Step 1: Extract Data Into A Staging Environment...3 Step 2: Transform Data Into the Target Salesforce Schema...5
More informationUsing GitHub to Share with SparkFun a
Using GitHub to Share with SparkFun a learn.sparkfun.com tutorial Available online at: http://sfe.io/t52 Contents Introduction Gitting Started Forking a Repository Committing, Pushing and Pulling Syncing
More informationAnimations involving numbers
136 Chapter 8 Animations involving numbers 8.1 Model and view The examples of Chapter 6 all compute the next picture in the animation from the previous picture. This turns out to be a rather restrictive
More informationMonitoring Java in Docker at CDK
CASE STUDY Monitoring Java in Docker at CDK The Digital Marketing business unit of CDK global shifted to a containerized approach for their next generation infrastructure. One of the challenges they ran
More informationWhat is Standard APEX? TOOLBOX FLAT DESIGN CARTOON PEOPLE
What is Standard APEX? TOOLBOX FLAT DESIGN CARTOON PEOPLE About me Freelancer since 2010 Consulting and development Oracle databases APEX BI Blog: APEX-AT-WORK Twitter: @tobias_arnhold - Oracle ACE Associate
More informationLegacy Transaction Integration TM In a Service-oriented Architecture (SOA)
November 2003 Legacy Transaction Integration TM In a Service-oriented Architecture (SOA) Introduction Over the next year or so, every serious IT analyst and software vendor will figuratively jump upon
More informationCustom Web & Mobile. Here are some of the ways Pulsetracker provides Sales and Marketing Intelligence:
Custom Web & Mobile SALES & MARKETING INTELLIGENCE Pulsetracker is an easy-to-use Sales & Marketing Customer Relationship Management (CRM) system designed for small to medium-sized businesses that do big
More information