Tales from Building a SQL Server Data Warehouse in Azure Experiences & Lessons Learned from a Migration to Azure

Total Page:16

File Type:pdf, Size:1020Kb

Tales from Building a SQL Server Data Warehouse in Azure Experiences & Lessons Learned from a Migration to Azure Tales from Building a SQL Server Data Warehouse in Azure Experiences & lessons learned from a migration to Azure August 21, 2017 Lead Analytics Architect, SentryOne Blog: www.sqlchick.com Microsoft Data Platform MVP Twitter: @sqlchick Melissa Coates Goals for This Session 1 Share decisions and lessons learned from a recent Azure implementation 2 Introduce key concepts for a deployment to Azure 3 Discuss items involved with building a DW environment in Azure Azure services & features change frequently. The information in the presentation is accurate as of mid-August 2017. Agenda Key Azure Concepts Deciding on Azure VM vs. Azure SQLDB vs. Azure SQLDW Additional Planning Considerations Building the Azure Environment Structuring Dev, Test, & Prod Environments Naming Conventions & Tags Out of scope: Configuration Decisions o Linux deployments Automation & Scheduling o Azure Stack & private cloud deployments Monitoring the Solution o On-premises physical & virtualized deployments (including Fast Track DW & APS/PDW) Key Takeaways & Open Q&A o Security in depth (though we touch on a few points) o Networking & connectivity o Troubleshooting, performance tuning & growth o Details on how to install and configure SQL Server Key Azure Concepts The Azure Lingo Azure Directory Subscription Subscription Resource Group Resource Group Subscription Resource Resource Resource Group Resource Resource Resource Group Subscription Azure Resource Manager (ARM) Azure Active Directory Active Directory https://azure.microsoft.com/en-us/overview/what-is-cloud-computing/ Office 365 Power BI https://azure.microsoft.com/en-us/overview/cloud-computing-dictionary/ Resource Groups Subscription Planning for resource groups is critical Resource Group Resource Group Resource Resource Resource Group Focus on: Resource Resource o Logical organization Resource Group o Permissions o Policies We have learned: ✓ Keep resource groups more narrow than broad ✓ Select the region (location) carefully Scope of ARM automation scripts (exception: Resource Explorer) Examples – Resources & Resource Groups IaaS PaaS IaaS vs. PaaS vs. SaaS Shared Public Cloud Software Highly Infrastructure as a Scalable (Lower Cost) Service Platform (SaaS) as a Service Infrastructure (Paas) Power BI, as a Service Office 365 (IaaS) Azure SQLDB & SQL Server in a Azure SQLDW Virtual Machine On-Premises Dedicated Virtual Azure Stack (Private Cloud) Infrastructure Server Physical (Higher Cost of Limits to Server Ownership) Scalability More Control Less Control (Higher Administration Effort) (Lower Administration Effort) Compute vs. Storage Compute Data Lake Analytics SQL Data Storage Warehouse HDInsight SQL Data Database SQL Data Warehouse SQL Server in a VM SQL Data Machine Database Learning Data Lake SQL Server in Store a VM Blob Storage Some resources scale, or even pause, compute separately from storage. Deciding on Azure VM vs. Azure SQLDB vs. Azure SQLDW Comparing the SQL Offerings in Azure (1/2) SQL Server in a Virtual Machine Azure SQL Database Azure SQL Data Warehouse (IaaS) (PaaS) (PaaS) Run full workload within an A relational database-as-a-service (DBaaS) An data warehouse-as-a- Azure virtual machine, service (DWaaS) optimized including SQL Server, SSIS, for performance and large- SSAS, SSRS, etc Non-Managed Managed Instance scale distributed workloads A traditional Azure Newer - closer feature MPP architecture SQLDB deployment parity to SQL Server (massively parallel (isolated DB) (instance level features) processing) Comparing the SQL Offerings in Azure (2/2) SQL Server in a Virtual Machine Azure SQL Database Azure SQL Data Warehouse (IaaS) (PaaS) (PaaS) Best for: Best for: Best for: ✓ Migrating existing solutions ✓New database solutions ✓DW with larger data volumes ✓ Running any software and/or (If a DW it should be < 4TB data size-- (bare min. of 1-4TB) sharding across DBs is not suitable for ✓Ability to scale compute all SQL Server features DW workloads) up/down, or pause (elasticity) ✓ Administering all aspects ✓OLTP with scaling & pooling ✓ Bring your own license ✓Data Lake Store integration needs (unpredictable workloads) (relational + nonrelational data) (Software Assurance) ✓Reduced administration of ✓ ✓Reduced administration Isolated dev/test environments DB, OS, HA, and DR ✓ ✓SLA: for the database SLA: for the VM ✓SLA: for the database More info: https://docs.microsoft.com/en-us/azure/sql-database/sql-database-paas-vs-sql-server-iaas Key Differences with Azure SQL Database Many features go first to Azure SQLDB (“cloud first”). However, there are some key features not available in SQLDB (PaaS): o PolyBase o R Services o Change data capture o CLR o DB snapshots o Some T-SQL syntax o Profiler Full list: o Non-primary filegroups https://docs.microsoft.com/en-us/azure/sql- database/sql-database-features Also, some features rely on Premium edition: o Columnstore indexes Key Differences with Azure SQL Data Warehouse (1/2) Control Node MPP Architecture Shared-Nothing Architecture Compute Compute Compute Node Node Node Decoupled Storage & Compute Data Storage Scale Up, Down, Pause PolyBase Key Differences with Azure SQL Data Warehouse (2/2) Not All Features Supported Control Node Different Data Loading Patterns Compute Compute Compute Distribution Keys are Critical Node Node Node Denormalized Data Model is Best Data Storage Take time to educate yourself on the key differences with the MPP architecture—it will affect the design & the data load processes Our Decisions on What to Use SQL Server in a Virtual Machine Azure SQL Database Azure SQL Data Warehouse (IaaS) (PaaS) (PaaS) We are using a VM for: We are using SQLDB for: We put SQLDW on roadmap: ✓ SQL Server DW ✓ A specific use case: public ✓ Future data growth ✓ Integration Services reporting solution via ✓ Future PolyBase ✓ Analysis Services (MD) SQLSkills Waits Library integration with multi- ✓ Master Data Services ✓ This SQLDB is loaded from structured data in Azure ✓ R Services the DW (in SQL Server) Data Lake Store Requires some refactoring of We need VMs for SSIS and SSAS the data load processes so we anyway, so we couldn’t justify are planning the move to migrating the relational DW to a SQLDW strategically PaaS solution at this time Our Current State IaaS PaaS Additional Planning Considerations Our Goals & Requirements for the Move to Azure Expand infrastructure to support future growth Ensure ability for Analytics Team to manage Support existing solutions with environment independently little to no redesign or refactoring Secure connectivity via VPN Minimize cost where practical Acceptable performance of hourly ETL jobs Think about trade-offs you’re willing to make for cost, performance, security, regulatory compliance, DR/backups/redundancy, and simplicity Initial Planning Before Provisioning Any Resources (1/3) o Licensing and Editions First Steps o Full Cloud Implementation vs. Hybrid (Partially On-Premises) o IaaS vs. PaaS decisions; feature comparison o Big 3: Storage, memory, CPU Capacity Planning o Networking & Cost Estimates o Scalability needs o Cost estimates: https://azure.microsoft.com/en-us/pricing/calculator/ High Availability & o Down time sensitivity (RPO, RTO) Disaster Recovery o SLAs from Azure Compliance & o Compliance: https://www.microsoft.com/en-us/trustcenter/compliance/default.aspx Security Initial Planning Before Provisioning Any Resources (2/3) o How administrative & owner permissions will work if decentralized Domain service o Read and/or write permissions for source or related systems accounts & o One domain service account per service, per environment credentials o Sync to Azure Active Directory for domain users & groups o Service principals for certain resources in Azure are very important o Geographic location of data o Proximity to business users Azure region: o Co-location of related resources primary location o Minimizing latency & o data redundancy Minimizing data egress charges (very inexpensive though) o Not all resources/services are available in every region Initial Planning Before Provisioning Any Resources (3/3) We went with a backup/restore approach for the Migration Method SQL Server files for the DW. The exception to this was SSISDB custom auditing Backup/ Upload & logging objects. This DDL was deployed from Restore a VHD SSDT after the SSISDB catalog was configured. Fail Over from Replication AlwaysOn Info on migration techniques: https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sql/virtual- machines-windows-migrate-sql Ship a Database Azure SQL Data Warehouse migration utility: Hard Drive Migration Service https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse- migrate-migration-utility Structuring Dev, Test & Prod Environments Separation of Dev, Test, Prod Environments Most commonly environments are segregated by: 1. Resource Groups, 2. Subscription, or 3. Directory, or 4. A combination of 1 and 3, or 2 and 3 Visual Dev Test Prod Studio Environment Environment Environment Deploy Deploy Deploy from via ARM via ARM Visual Template Template Studio For large or multi-tenant implementations: be aware of Azure limits before deciding. Option: Separate By Directory Dev Directory Test Directory Prod Directory Pros: ✓ Clear boundary Subscription Subscription Subscription ✓ Offers the most scalability Cons: ✓ Resource Group: Resource Group: Resource Group: More infrastructure to Internal Rptg Internal Rptg Internal Rptg manage ✓ A lot of objects intermixed in a subscription - need Resource Group: Resource Group: Resource Group: clear resource
Recommended publications
  • Data Warehouse Fundamentals for Storage Professionals – What You Need to Know EMC Proven Professional Knowledge Sharing 2011
    Data Warehouse Fundamentals for Storage Professionals – What You Need To Know EMC Proven Professional Knowledge Sharing 2011 Bruce Yellin Advisory Technology Consultant EMC Corporation [email protected] Table of Contents Introduction ................................................................................................................................ 3 Data Warehouse Background .................................................................................................... 4 What Is a Data Warehouse? ................................................................................................... 4 Data Mart Defined .................................................................................................................. 8 Schemas and Data Models ..................................................................................................... 9 Data Warehouse Design – Top Down or Bottom Up? ............................................................10 Extract, Transformation and Loading (ETL) ...........................................................................11 Why You Build a Data Warehouse: Business Intelligence .....................................................13 Technology to the Rescue?.......................................................................................................19 RASP - Reliability, Availability, Scalability and Performance ..................................................20 Data Warehouse Backups .....................................................................................................26
    [Show full text]
  • Master Data Management Whitepaper.Indd
    Creating a Master Data Environment An Incremental Roadmap for Public Sector Organizations Master Data Management (MDM) is the processes and technologies used to create and maintain consistent and accurate representations of master data. Movements toward modularity, service orientation, and SaaS make Master Data Management a critical issue. Master Data Management leverages both tools and processes that enable the linkage of critical data through a unifi ed platform that provides a common point of reference. When properly done, MDM streamlines data management and sharing across the enterprise among different areas to provide more effective service delivery. CMA possesses more than 15 years of experience in Master Data Management and more than 20 in data management and integration. CMA has also focused our efforts on public sector since our inception, more than 30 years ago. This document describes the fundamental keys to success for developing and maintaining a long term master data program within a public service organization. www.cma.com 2 Understanding Master Data transactional data. As it becomes more volatile, it typically is considered more transactional. Simple Master Data Behavior entities are rarely a challenge to manage and are rarely Master data can be described by the way that it interacts considered master-data elements. The less complex an with other data. For example, in transaction systems, element, the less likely the need to manage change for master data is almost always involved with transactional that element. The more valuable the data element is to data. This relationship between master data and the organization, the more likely it will be considered a transactional data may be fundamentally viewed as a master data element.
    [Show full text]
  • Data Warehouse Applications in Modern Day Business
    California State University, San Bernardino CSUSB ScholarWorks Theses Digitization Project John M. Pfau Library 2002 Data warehouse applications in modern day business Carla Mounir Issa Follow this and additional works at: https://scholarworks.lib.csusb.edu/etd-project Part of the Business Intelligence Commons, and the Databases and Information Systems Commons Recommended Citation Issa, Carla Mounir, "Data warehouse applications in modern day business" (2002). Theses Digitization Project. 2148. https://scholarworks.lib.csusb.edu/etd-project/2148 This Project is brought to you for free and open access by the John M. Pfau Library at CSUSB ScholarWorks. It has been accepted for inclusion in Theses Digitization Project by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [email protected]. DATA WAREHOUSE APPLICATIONS IN MODERN DAY BUSINESS A Project Presented to the Faculty of California State University, San Bernardino In Partial Fulfillment of the Requirements for the Degree Master of Business Administration by Carla Mounir Issa June 2002 DATA WAREHOUSE APPLICATIONS IN MODERN DAY BUSINESS A Project Presented to the Faculty of California State University, San Bernardino by Carla Mounir Issa June 2002 Approved by: Date Dr. Walter T. Stewart ABSTRACT Data warehousing is not a new concept in the business world. However, the constant changing application of data warehousing is what is being innovated constantly. It is these applications that are enabling managers to make better decisions through strategic planning, and allowing organizations to achieve a competitive advantage. The technology of implementing the data warehouse will help with the decision making process, analysis design, and will be more cost effective in the future.
    [Show full text]
  • Lecture Notes on Data Mining& Data Warehousing
    LECTURE NOTES ON DATA MINING& DATA WAREHOUSING COURSE CODE:BCS-403 DEPT OF CSE & IT VSSUT, Burla SYLLABUS: Module – I Data Mining overview, Data Warehouse and OLAP Technology,Data Warehouse Architecture, Stepsfor the Design and Construction of Data Warehouses, A Three-Tier Data WarehouseArchitecture,OLAP,OLAP queries, metadata repository,Data Preprocessing – Data Integration and Transformation, Data Reduction,Data Mining Primitives:What Defines a Data Mining Task? Task-Relevant Data, The Kind of Knowledge to be Mined,KDD Module – II Mining Association Rules in Large Databases, Association Rule Mining, Market BasketAnalysis: Mining A Road Map, The Apriori Algorithm: Finding Frequent Itemsets Using Candidate Generation,Generating Association Rules from Frequent Itemsets, Improving the Efficiently of Apriori,Mining Frequent Itemsets without Candidate Generation, Multilevel Association Rules, Approaches toMining Multilevel Association Rules, Mining Multidimensional Association Rules for Relational Database and Data Warehouses,Multidimensional Association Rules, Mining Quantitative Association Rules, MiningDistance-Based Association Rules, From Association Mining to Correlation Analysis Module – III What is Classification? What Is Prediction? Issues RegardingClassification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Bayes Theorem, Naïve Bayesian Classification, Classification by Backpropagation, A Multilayer Feed-Forward Neural Network, Defining aNetwork Topology, Classification Based of Concepts from
    [Show full text]
  • Database Vs. Data Warehouse: a Comparative Review
    Insights Database vs. Data Warehouse: A Comparative Review By Drew Cardon Professional Services, SVP Health Catalyst A question I often hear out in the field is: I already have a database, so why do I need a data warehouse for healthcare analytics? What is the difference between a database vs. a data warehouse? These questions are fair ones. For years, I’ve worked with databases in healthcare and in other industries, so I’m very familiar with the technical ins and outs of this topic. In this post, I’ll do my best to introduce these technical concepts in a way that everyone can understand. But, before we discuss the difference, could I ask one big favor? This will only take 10 seconds. Could you click below and take a quick poll? I’d like to find out if your organization has a data warehouse, data base(s), or if you don’t know? This would really help me better understand how prevalent data warehouses really are. Click to take our 10 second database vs data warehouse poll Before diving in to the topic, I want to quickly highlight the importance of analytics in healthcare. If you don’t understand the importance of analytics, discussing the distinction between a database and a data warehouse won’t be relevant to you. Here it is in a nutshell. The future of healthcare depends on our ability to use the massive amounts of data now available to drive better quality at a lower cost. If you can’t perform analytics to make sense of your data, you’ll have trouble improving quality and costs, and you won’t succeed in the new healthcare environment.
    [Show full text]
  • MASTER's THESIS Role of Metadata in the Datawarehousing Environment
    2006:24 MASTER'S THESIS Role of Metadata in the Datawarehousing Environment Kranthi Kumar Parankusham Ravinder Reddy Madupu Luleå University of Technology Master Thesis, Continuation Courses Computer and Systems Science Department of Business Administration and Social Sciences Division of Information Systems Sciences 2006:24 - ISSN: 1653-0187 - ISRN: LTU-PB-EX--06/24--SE Preface This study is performed as the part of the master’s programme in computer and system sciences during 2005-06. It has been very useful and valuable experience and we have learned a lot during the study, not only about the topic at hand but also to manage to the work in the specified time. However, this workload would not have been manageable if we had not received help and support from a number of people who we would like to mention. First of all, we would like to thank our professor Svante Edzen for his help and supervision during the writing of thesis. And also we would like to express our gratitude to all the employees who allocated their valuable time to share their professional experience. On a personal level, Kranthi would like to thank all his family for their help and especially for my friends Kiran, Chenna Reddy, and Deepak Kumar. Ravi would like to give the greatest of thanks to his family for always being there when needed, and constantly taking so extremely good care of me….Also, thanks to all my friends for being close to me. Luleå University of Technology, 31 January 2006 Kranthi Kumar Parankusham Ravinder Reddy Madupu Abstract In order for a well functioning data warehouse to succeed many components must work together.
    [Show full text]
  • The Teradata Data Warehouse Appliance Technical Note on Teradata Data Warehouse Appliance Vs
    The Teradata Data Warehouse Appliance Technical Note on Teradata Data Warehouse Appliance vs. Oracle Exadata By Krish Krishnan Sixth Sense Advisors Inc. November 2013 Sponsored by: Table of Contents Background ..................................................................3 Loading Data ............................................................3 Data Availability .........................................................3 Data Volumes ............................................................3 Storage Performance .....................................................4 Operational Costs ........................................................4 The Data Warehouse Appliance Selection .....................................5 Under the Hood ..........................................................5 Oracle Exadata. .5 Oracle Exadata Intelligent Storage ........................................6 Hybrid Columnar Compression ...........................................7 Mixed Workload Capability ...............................................8 Management ............................................................9 Scalability ................................................................9 Consolidation ............................................................9 Supportability ...........................................................9 Teradata Data Warehouse Appliance .........................................10 Hardware Improvements ................................................10 Teradata’s Intelligent Memory (TIM) vs. Exdata Flach Cache.
    [Show full text]
  • Enterprise Data Warehouse (EDW) Platform PRIVACY IMPACT ASSESSMENT (PIA)
    U.S. Securities and Exchange Commission Enterprise Data Warehouse (EDW) Platform PRIVACY IMPACT ASSESSMENT (PIA) May 31, 2020 Office of Information Technology Privacy Impact Assessment Enterprise Data Warehouse Platform (EDW) General Information 1. Name of Project or System. Enterprise Data Warehouse (EDW) Platform 2. Describe the project and its purpose or function in the SEC’s IT environment. The Enterprise Data Warehouse (EDW) infrastructure will enable the provisioning of data to Commission staff for search and analysis through a virtual data warehouse platform. As part of its Enterprise Data Warehouse initiative, the SEC will begin to integrate data from various systems to provide more comprehensive management and financial reporting on a regular basis and facilitate better decision-making. (SEC Strategic Plan, 2014-2018, pg.31) The EDW will replicate source system-provided data from other operational SEC systems and provide a simplified way of producing Agency reports. The EDW is designed for summarization of information, retrieval of information, reporting speed, and ease of use (i.e. creating reports). It will enhance business intelligence, augment data quality and consistency, and generate a high return on investment by allowing users to quickly search and access critical data from a single location and obtain historical intelligence to analyze different time periods and performance trends in order to make future predictions. (SEC FY2013 Agency Financial Report, pg.31-32) The data content in the Enterprise Data Warehouse platform will be provisioned incrementally over time via a series of distinct development projects, each of which will target specific data sets based on requirements provided by SEC business stakeholders.
    [Show full text]
  • High-Performance Analytics for Today's Business
    Teradata Database: High-Performance Analytics for Today’s Business DATA WAREHOUSING Because Performance Counts Already the foundation of the world’s most successful data warehouses, Teradata Database is designed for decision support and parallel implementation. And it’s as versatile The success of your data warehouse has always rested as it is powerful. It supports hundreds of installations, on the performance of your database engine. But as from three terabytes to huge warehouses with petabytes your analytics and data requirements grow increasingly (PB) of data and thousands of users. Teradata Database’s complex, performance becomes more vital than ever. inherent parallelism and architecture provide more So does finding a faster, simpler way to manage your data than pure performance. It also offers low total cost of warehouse. You can find that combination of unmatched ownership (TCO) and a simple, scalable, and self-managing performance, flexibility, and efficient management in the solution for building a successful data warehouse. Teradata® Database. With the Teradata Software-Defined Warehouse, In fact, we continually enhance the Teradata Database customers have the flexibility to segregate data and to provide you and your business with new features users on a single system. By combining Teradata and functions for even higher levels of flexibility and Database’s secure zones with Teradata’s industry-leading performance. Work with dynamic data and schema-on- mixed workload management capabilities, multi-tenant read operational models together with structured data economies and flexible privacy regulation compliance through completely integrated JSON and Avro data. become possible in a single Teradata Database system. Query processing performance is also accelerated by the world’s most advanced hybrid row/column table storage capability and in-memory optimization and vectorization for in-memory database speed.
    [Show full text]
  • Olap & Data Mining
    OLAP & DATA MINING CS561-SPRING 2012 WPI, MOHAMED ELTABAKH 1 Online Analytic Processing OLAP 2 OLAP • OLAP: Online Analytic Processing • OLAP queries are complex queries that • Touch large amounts of data • Discover patterns and trends in the data • Typically expensive queries that take long time • Also called decision-support queries • In contrast to OLAP: • OLTP: Online Transaction Processing • OLTP queries are simple queries, e.g., over banking or airline systems • OLTP queries touch small amount of data for fast transactions 3 OLTP vs. OLAP § On-Line Transaction Processing (OLTP): – technology used to perform updates on operational or transactional systems (e.g., point of sale systems) § On-Line Analytical Processing (OLAP): – technology used to perform complex analysis of the data in a data warehouse OLAP is a category of software technology that enables analysts, managers, and executives to gain insight into data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the dimensionality of the enterprise as understood by the user. [source: OLAP Council: www.olapcouncil.org] 4 OLAP AND DATA WAREHOUSE OLAP Server OLAP Internal Sources Reports Data Data Query and Integration Warehouse Analysis Operational Component Component DBs Data Mining Meta data External Client Sources Tools 5 OLAP AND DATA WAREHOUSE • Typically, OLAP queries are executed over a separate copy of the working data • Over data warehouse • Data warehouse is periodically updated,
    [Show full text]
  • Lineage Tracing for General Data Warehouse Transformations
    Lineage Tracing for General Data Warehouse Transformations∗ Yingwei Cui and Jennifer Widom Computer Science Department, Stanford University fcyw, [email protected] Abstract. Data warehousing systems integrate information and managing such transformations as part of the extract- from operational data sources into a central repository to enable transform-load (ETL) process, e.g., [Inf, Mic, PPD, Sag]. analysis and mining of the integrated information. During the The transformations may vary from simple algebraic op- integration process, source data typically undergoes a series of erations or aggregations to complex procedural code. transformations, which may vary from simple algebraic opera- In this paper we consider the problem of lineage trac- tions or aggregations to complex “data cleansing” procedures. ing for data warehouses created by general transforma- In a warehousing environment, the data lineage problem is that tions. Since we no longer have the luxury of a fixed set of of tracing warehouse data items back to the original source items operators or the algebraic properties offered by relational from which they were derived. We formally define the lineage views, the problem is considerably more difficult and tracing problem in the presence of general data warehouse trans- open-ended than previous work on lineage tracing. Fur- formations, and we present algorithms for lineage tracing in this thermore, since transformation graphs in real ETL pro- environment. Our tracing procedures take advantage of known cesses can often be quite complex—containing as many structure or properties of transformations when present, but also as 60 or more transformations—the storage requirements work in the absence of such information.
    [Show full text]
  • Getting Started with SAP SQL Data Warehousing
    Getting Started Guide CUSTOMER Document Version: 1.0 – 2018-05-29 Getting Started with SAP SQL Data Warehousing Table of Contents 1 Solution Information................................................................................................................ 3 Installed Products ................................................................................................................................................... 3 Overview ................................................................................................................................................................. 3 Scenario Description ...............................................................................................Error! Bookmark not defined. 2 Accessing the Solution ............................................................................................................4 Overview ................................................................................................................................................................. 4 Windows Frontend Server Details .................................................................................................................... 6 ABAP Application Server Details ...................................................................................................................... 6 Java Application Server Details ..........................................................................Error! Bookmark not defined. Database Server Details ......................................................................................................................................
    [Show full text]