Database Design Quotes

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I found myself answering the same questions asked frequently of me by different people. It would be so much easier if everyone could just read my database.
Tim Berners-Lee (Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web)
A database is just a tool: how you use it is up to you.
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
She was designed to look human, her face the replica of a woman whose image Med’s tissue engineer had licensed from an old Facebook database.
Annalee Newitz (Autonomous)
generally speaking, no one should be directly logging in to a database.
Laine Campbell (Database Reliability Engineering: Designing and Operating Resilient Database Systems)
At this risk of being redundant, everything must be checked in to code. This includes the following: DB object migrations Triggers Procedures and functions Views Configurations Sample datasets for functionality Data cleanup scripts
Laine Campbell (Database Reliability Engineering: Designing and Operating Resilient Database Systems)
Different databases are designed to solve different problems. Using a single database engine for all of the requirements usually leads to non- performant solutions; storing transactional data, caching session information, traversing graph of customers and the products their friends bought are essentially different problems.
Pramod J. Sadalage (NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence)
It’s in statues: when I counted all the statues in the UK’s Public Monuments and Sculptures Association database I found that there were more statues of men called John than there were of historical, named, non-royal women (the only reason adding royal women to the figure just beats the Johns is down to Queen Victoria, whose enthusiasm for putting up statues of herself I have a grudging respect for).
Caroline Criado Pérez (Invisible Women: Exposing Data Bias in a World Designed for Men)
Oath of Non-Harm for an Age of Big Data I swear to fulfill, to the best of my ability, the following covenant: I will respect all people for their integrity and wisdom, understanding that they are experts in their own lives, and will gladly share with them all the benefits of my knowledge. I will use my skills and resources to create bridges for human potential, not barriers. I will create tools that remove obstacles between resources and the people who need them. I will not use my technical knowledge to compound the disadvantage created by historic patterns of racism, classism, able-ism, sexism, homophobia, xenophobia, transphobia, religious intolerance, and other forms of oppression. I will design with history in mind. To ignore a four-century-long pattern of punishing the poor is to be complicit in the “unintended” but terribly predictable consequences that arise when equity and good intentions are assumed as initial conditions. I will integrate systems for the needs of people, not data. I will choose system integration as a mechanism to attain human needs, not to facilitate ubiquitous surveillance. I will not collect data for data’s sake, nor keep it just because I can. When informed consent and design convenience come into conflict, informed consent will always prevail. I will design no data-based system that overturns an established legal right of the poor. I will remember that the technologies I design are not aimed at data points, probabilities, or patterns, but at human beings.
Virginia Eubanks (Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor)
One of the patterns from domain-driven design is called bounded context. Bounded contexts are used to set the logical boundaries of a domain’s solution space for better managing complexity. It’s important that teams understand which aspects, including data, they can change on their own and which are shared dependencies for which they need to coordinate with other teams to avoid breaking things. Setting boundaries helps teams and developers manage the dependencies more efficiently. The logical boundaries are typically explicit and enforced on areas with clear and higher cohesion. These domain dependencies can sit on different levels, such as specific parts of the application, processes, associated database designs, etc. The bounded context, we can conclude, is polymorphic and can be applied to many different viewpoints. Polymorphic means that the bounded context size and shape can vary based on viewpoint and surroundings. This also means you need to be explicit when using a bounded context; otherwise it remains pretty vague.
Piethein Strengholt (Data Management at Scale: Best Practices for Enterprise Architecture)
Now imagine if, instead of accidentally leaving open a loophole, the hackers behind WannaCry had designed the program to systematically learn about its own vulnerabilities and repeatedly patch them. Imagine if, as it attacked, the program evolved to exploit further weaknesses. Imagine that it then started moving through every hospital, every office, every home, constantly mutating, learning. It could hit life-support systems, military infrastructure, transport signaling, the energy grid, financial databases. As it spread, imagine the program learning to detect and stop further attempts to shut it down. A weapon like this is on the
Mustafa Suleyman (The Coming Wave: AI, Power, and Our Future)
Probably my favorite method of funding school, other than saving for it, is scholarships. There is a dispute as to how many scholarships go unclaimed every year. Certainly there are people on the Web who will hype you on this subject. However, legitimately there are hundreds of millions of dollars in scholarships given out every year. These scholarships are not academic or athletic scholarships either. They are of small- to medium-sized dollar amounts from organizations like community clubs. The Rotary Club, the Lions Club, or the Jaycees many times have $250 or $500 per year they award to some good young citizen. Some of these scholarships are based on race or sex or religion. For instance, they might be designed to help someone with Native American heritage get an education. The lists of these scholarships can be bought online, and there are even a few software programs you can purchase. Denise, a listener to my show, took my advice, bought one of the software programs, and worked the system. That particular software covered more than 300,000 available scholarships. She narrowed the database search until she had 1,000 scholarships to apply for. She spent the whole summer filling out applications and writing essays. She literally applied for 1,000 scholarships. Denise was turned down by 970, but she got 30, and those 30 scholarships paid her $38,000. She went to school for free while her next-door neighbor sat and whined that no money was available for school and eventually got a student loan.
Dave Ramsey (The Total Money Makeover: A Proven Plan for Financial Fitness)
Grabbing my hair and pulling it to the point my skull throbs, I rock back and forth while insanity threatens to destroy my mind completely. Father finally did what Lachlan started. Destroyed my spirit. The angel is gone. The monster has come and killed her. Lachlan Sipping his whiskey, Shon gazes with a bored expression at the one-way mirror as Arson lights the match, grazing the skin of his victim with it as the man convulses in fear. “Show off,” he mutters, and on instinct, I slap the back of his head. He rubs it, spilling the drink. “The fuck? We are wasting time, Lachlan. Tell him to speed up. You know if you let him, he can play for hours.” All in good time, we don’t need just a name. He is saving him for a different kind of information that we write down as Sociopath types furiously on his computer, searching for the location and everything else using FBI databases. “Bingo!” Sociopath mutters, picking up the laptop and showing the screen to me. “It’s seven hours away from New York, in a deserted location in the woods. The land belongs to some guy who is presumed dead and the man accrued the right to build shelters for abused women. They actually live there as a place of new hope or something.” Indeed, the center is advertised as such and has a bunch of stupid reviews about it. Even the approval of a social worker, but then it doesn’t surprise me. Pastor knows how to be convincing. “Kids,” I mutter, fisting my hands. “Most of them probably have kids. He continues to do his fucked-up shit.” And all these years, he has been under my radar. I throw the chair and it bounces off the wall, but no one says anything as they feel the same. “Shon, order a plane. Jaxon—” “Yeah, my brothers will be there with us. But listen, the FBI—” he starts, and I nod. He takes a beat and quickly sends a message to someone on his phone while I bark into the microphone. “Arson, enough with the bullshit. Kill him already.” He is of no use to us anyway. Arson looks at the wall and shrugs. Then pours gas on his victim and lights up the match simultaneously, stepping aside as the man screams and thrashes on the chair, and the smell of burning flesh can be sensed even here. Arson jogs to a hose, splashing water over him. The room is designed security wise for this kind of torture, since fire is one of the first things I taught. After all, I’d learned the hard way how to fight with it. “On the plane, we can adjust the plan. Let’s get moving.” They spring into action as I go to my room to get a specific folder to give to Levi before I go, when Sociopath’s hand stops me, bumping my shoulder. “Is this a suicide mission for you?” he asks, and I smile, although it lacks any humor. My friend knows everything. Instead of answering his question, I grip his shoulder tight, and confide, “Valencia is entrusted to you.” We both know that if I want to destroy Pastor, I have to die with him. This revenge has been twenty-three years in the making, and I never envisioned a different future. This path always leads to death one way or another, and the only reason I valued my life was because I had to kill him. Valencia will be forever free from the evils that destroyed her life. I’ll make sure of it. Once upon a time, there was an angel. Who made the monster’s heart bleed.
V.F. Mason (Lachlan's Protégé (Dark Protégés #1))
Databases should usually not be considered as a major factor of the design and implementation.
Robert C. Martin (Agile Principles, Patterns, and Practices in C# (Robert C. Martin Series))
With the microservices design, where each distinguished functional component is developed into a microservice, based on their business capabilities, will have its own database—so each such service can scale end to end without having any dependency on other microservices.
Prabath Siriwardena (Advanced API Security: OAuth 2.0 and Beyond)
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Unfortunately, the SQL standard’s definition of isolation levels is flawed—it is ambiguous, imprecise, and not as implementation-independent as a standard should be [28]. Even though several databases implement repeatable read, there are big differences in the guarantees they actually provide, despite being ostensibly standardized [23]. There has been a formal definition of repeatable read in the research literature [29, 30], but most implementations don’t satisfy that formal definition. And to top it off, IBM DB2 uses “repeatable read” to refer to serializability [8]. As a result, nobody really knows what repeatable read means.
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
Although the REPOSITORY will insert into and delete from the database, it will ordinarily not commit anything. It is tempting to commit after saving, for example, but the client presumably has the context to correctly initiate and commit units of work. Transaction management will be simpler if the REPOSITORY keeps its hands off.
Evans Eric (Domain-Driven Design: Tackling Complexity in the Heart of Software)
Read replicas This technique follows a master-slave architecture. Here, database tables are distributed across different sets of nodes, of which some are known as slaves and one as master. Master node handles write queries while read queries are routed to slave nodes. The model works best if data changes are limited but read queries are large. To handle an unexpected scenario like a master failure, one of the slave nodes can be elevated to the master node. The pattern works well to scale out read queries
Harsh Kumar Ramchandani (Hands-On System Design: Learn System Design, Scaling Applications, Software Development Design Patterns with Real Use-Cases (English Edition))
Sharding is a technique where rows of a database table are held separately. It reduces the size of the index table; thus, the processing time of queries may improve significantly.
Harsh Kumar Ramchandani (Hands-On System Design: Learn System Design, Scaling Applications, Software Development Design Patterns with Real Use-Cases (English Edition))
I think you get the point. If you can develop the high-level policy without committing to the details that surround it, you can delay and defer decisions about those details for a long time. And the longer you wait to make those decisions, the more information you have with which to make them properly. This also leaves you the option to try different experiments. If you have a portion of the high-level policy working, and it is agnostic about the database, you could try connecting it to several different databases to check applicability and performance. The same is true with web systems, web frameworks, or even the web itself. The longer you leave options open, the more experiments you can run, the more things you can try, and the more information you will have when you reach the point at which those decisions can no longer be deferred.
Robert C. Martin (Clean Architecture: A Craftsman's Guide to Software Structure and Design)
Transactional outbox: None of the preceding methods are completely foolproof in the sense that there still exists a window of opportunity where the database and the event bus can become inconsistent (this is true even with two-phase commits). One way to circumvent this problem is by completely eliminating the dual-write problem. In this solution, the command processor writes to its database and the intended event to an outbox table in a local transaction. A separate poller component polls the outbox table and writes to the event bus. Polling can be computationally intensive and could lead back to the dual write problem again because the poller has to keep track of the last written event.
Premanand Chandrasekaran (Domain-Driven Design with Java - A Practitioner's Guide: Create simple, elegant, and valuable software solutions for complex business problems)
Cloud Service Any service made available to users on demand via the Internet from a cloud computing provider's servers rather than from a company's own on-premises servers is referred to as a cloud service. Cloud services are managed by a cloud services provider and are designed to enable easy, scalable access to applications, resources, and services. CLOUD SERVICES ARE SCALABLE AND DYNAMIC. A cloud service may dynamically scale to meet the needs of its customers, and because the service provider provides all of the necessary hardware and software, a corporation does not need to provision or deploy its own resources or dedicate IT people to operate the service. Online data storage and backup solutions, Web-based e-mail services, hosted office suites and document collaboration services, database processing, managed technical support services, and more are all examples of cloud services.
Crafsol Technology
The constraints of a toolset help to define patterns for solving problems. In the case of MongoDB, one of those constraints is the lack of atomic multidocument update operations. The patterns we use in MongoDB to mitigate the lack of atomic multidocument update operations include document embedding and complex updates for basic operations, with optimistic update with compensation available for when we really need a two-phase commit protocol. When designing your application to use MongoDB, more than in relational databases, you must keep in mind which updates you need to be atomic and design your schema appropriately.
Rick Copeland (MongoDB Applied Design Patterns)
MongoDB was designed from the ground up to be easy to scale to multiple distributed servers. Two of the biggest problems in distributed database design are distributed join operations and distributed transactions.
Rick Copeland (MongoDB Applied Design Patterns)
MongoDB is sometimes referred to as a “schemaless” database, meaning that it does not enforce a particular structure on documents in a collection. It is perfectly legal (though of questionable utility) to store every object in your application in the same collection, regardless of its structure. In
Rick Copeland (MongoDB Applied Design Patterns)
Index design is also a largely iterative process, based on the SQL generated by application designers. However, it is possible to make a sensible start by building indexes that enforce primary key constraints and indexes on known access patterns, such as a person's name. As the application evolves and testing is performed on realistic sizes of data, certain queries will need performance improvements for which building a better index is a good solution.
Andrew Holdsworth (Oracle9i Database Performance Planning)
[...] exhibition designers must be able to manage and work creatively with the digital information that surrounds and exhibition. Increasingly, the assets of a museum will include an electronic database that can be accessed through websites, apps and creative interactive devices within the environs of a gallery interior, in addition to traditional object displays.
Philip Hughes (Exhibition Design)
I have seen perhaps as many country codes stored in databases (shown in Figure 5-4) as I have written StringUtils classes for in-house Java projects. This seems to imply that we plan to change the countries our system supports way more frequently than we’ll deploy new code,
Sam Newman (Building Microservices: Designing Fine-Grained Systems)
Each business process is represented by a dimensional model that consists of a fact table containing the event's numeric measurements surrounded by a halo of dimension tables that contain the textual context that was true at the moment the event occurred. This characteristic star-like structure is often called a star join, a term dating back to the earliest days of relational databases. Figure 1.5 Fact and dimension tables in a dimensional model. The first thing to notice about the dimensional schema is its simplicity and symmetry. Obviously, business users benefit from the simplicity because the data is easier to understand and navigate. The charm of the design in Figure 1.5 is that it is highly recognizable to business users. We have observed literally hundreds of instances in which users immediately agree that the dimensional model is their business. Furthermore, the reduced number of tables and use of meaningful business descriptors make it easy to navigate and less likely that mistakes will occur. The simplicity of a dimensional model also has performance benefits. Database optimizers process these simple schemas with fewer joins more efficiently. A database engine can make strong assumptions about first constraining the heavily indexed dimension tables, and then attacking the fact table all at once with the Cartesian product of the dimension table keys satisfying the user's constraints. Amazingly, using this approach, the optimizer can evaluate arbitrary n-way joins to a fact table in a single pass through the fact table's index. Finally, dimensional models are gracefully extensible to accommodate change. The predictable framework of a dimensional model withstands unexpected changes in user behavior. Every dimension is equivalent; all dimensions are symmetrically-equal entry points into the fact table. The dimensional model has no built-in bias regarding expected query patterns. There are no preferences for the business questions asked this month versus the questions asked next month. You certainly don't want to adjust schemas if business users suggest new ways to analyze their business.
Ralph Kimball (The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling)
Dimensional models implemented in relational database management systems are referred to as star schemas because of their resemblance to a star-like structure. Dimensional models implemented in multidimensional database environments are referred to as online analytical processing (OLAP) cubes, as illustrated in Figure 1.1. Figure 1.1 Star schema versus OLAP cube. If your DW/BI environment includes either star schemas or OLAP cubes, it leverages dimensional concepts. Both stars and cubes have a common logical design with recognizable dimensions; however, the physical implementation differs. When data is loaded into an OLAP cube, it is stored and indexed using formats and techniques that are designed for dimensional data. Performance aggregations or precalculated summary tables are often created and managed by the OLAP cube engine. Consequently, cubes deliver superior query performance because of the precalculations, indexing strategies, and other optimizations. Business users can drill down or up by adding or removing attributes from their analyses with excellent performance without issuing new queries. OLAP cubes also provide more analytically robust functions that exceed those available with SQL. The downside is that you pay a load performance price for these capabilities, especially with large data sets.
Ralph Kimball (The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling)
Q: How many Java programmers does it take to change a lightbulb? A: One, to generate a “ChangeLightBulb” event to the socket. Q: How many C++ programmers does it take to change a lightbulb? A: You’re still thinking procedurally. A well-designed lightbulb object would inherit a change method from a generic lightbulb class. Q: How many Windows programmers does it take to change a lightbulb? A: Three. One to write WinGetLightBulbHandle, one to write WinQueryStatusLightBulb, and one to write WinGetLight-SwitchHandle. Q: How many database programmers does it take to change a lightbulb? A: Three. One to write the lightbulb removal program, one to write the lightbulb insertion program, and one to act as a lightbulb administrator to make sure nobody else tries to change the light-bulb at the same time. Q: How many software engineers does it take to change a lightbulb? A: None. That’s a hardware problem. Q: How many Microsoft engineers does it take to change a lightbulb? A: None. They will redefine darkness as an industry standard.
Scott McNeely (Ultimate Book of Jokes: The Essential Collection of More Than 1,500 Jokes)
ThreadLocal, which allows you to associate a per-thread value with a value-holding object. Thread-Local provides get and set accessormethods that maintain a separate copy of the value for each thread that uses it, so a get returns the most recent value passed to set from the currently executing thread. Thread-local variables are often used to prevent sharing in designs based on mutable Singletons or global variables. For example, a single-threaded application might maintain a global database connection that is initialized at startup to avoid having to pass a Connection to every method. Since JDBC connections may not be thread-safe, a multithreaded application that uses a global connection without additional coordination is not thread-safe either. By using a ThreadLocal to store the JDBC connection, as in ConnectionHolder in Listing 3.10, each thread will have its own connection. Listing
Brian Goetz (Java Concurrency in Practice)
Remember when we talked about the core principles behind good microservices? Strong cohesion and loose coupling — with database integration, we lose both things.
Sam Newman (Building Microservices: Designing Fine-Grained Systems)
Services should also hide their databases to avoid falling into one of the most common sorts of coupling that can appear in traditional service-oriented architectures, and use data pumps or event data pumps to consolidate data across multiple services for reporting purposes.
Sam Newman (Building Microservices: Designing Fine-Grained Systems)
Avoid database integration at all costs. Understand the trade-offs between REST and RPC, but strongly consider REST as a good starting point for request/response integration. Prefer choreography over orchestration. Avoid breaking changes and the need to version by understanding Postel’s Law and using tolerant readers.
Sam Newman (Building Microservices: Designing Fine-Grained Systems)
although experience has shown that it is far easier to push out changes to configuration files than alter live database tables. This is often a very sensible approach.
Sam Newman (Building Microservices: Designing Fine-Grained Systems)
Social marketing is distinguished from other planning frameworks discussed in this book by four principles: (1) a commitment to create satisfying exchanges, (2) the use of marketing’s conceptual framework to design interventions, (3) a data-based consumer orientation, and (4) segmentation of populations and careful selection of target audiences.
M. Jeannine Coreil (Social and Behavioral Foundations of Public Health)
Complex environments for social interventions and innovations are those in which what to do to solve problems is uncertain and key stakeholders are in conflict about how to proceed. Informed by systems thinking and sensitive to complex nonlinear dynamics, developmental evaluation supports social innovation and adaptive management. Evaluation processes include asking evaluative questions, applying evaluation logic, and gathering realtime data to inform ongoing decision making and adaptations. The evaluator is often part of a development team whose members collaborate to conceptualize, design, and test new approaches in a long-term, ongoing process of continuous development, adaptation, and experimentation, keenly sensitive to unintended results and side effects. The evaluator’s primary function in the team is to infuse team discussions with evaluative questions, thinking, and data, and to facilitate systematic data-based reflection
Michael Quinn Patton (Developmental Evaluation: Applying Complexity Concepts to Enhance Innovation and Use)
One of the most critical design and architecture decisions adopters of advanced analytics must make is whether to store analytic data in a data warehouse or in a standalone analytic database. Where does the data go? Where is it managed? Where are we going to do our analytical processes? Philip Russom, Senior Manager, TDWI Research
Keith Holdaway (Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data-Driven Models (Wiley and SAS Business Series))
The safety of user information stored in databases is often overlooked during the design of web applications.
Miguel Grinberg (Flask Web Development: Developing Web Applications with Python)
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The layered architecture is a great example of an architectural style, but it does have some significant drawbacks: Single presentation layer—It doesn’t represent the fact that an application is likely to be invoked by more than just a single system. Single persistence layer—It doesn’t represent the fact that an application is likely to interact with more than just a single database. Defines the business logic layer as depending on the persistence layer—In theory, this dependency prevents you from testing the business logic without the database. Also, the layered architecture misrepresents the dependencies in a well-designed application.
Chris Richardson (Microservices Patterns: With examples in Java)
Historically, data started out being represented as one big tree (the hierarchical model), but that wasn’t good for representing many-to-many relationships, so the relational model was invented to solve that problem. More recently, developers found that some applications don’t fit well in the relational model either. New nonrelational “NoSQL” datastores have diverged in two main directions: Document databases target use cases where data comes in self-contained documents and relationships between one document and another are rare. Graph databases go in the opposite direction, targeting use cases where anything is potentially related to everything.
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
All three models (document, relational, and graph) are widely used today, and each is good in its respective domain. One model can be emulated in terms of another model—for example, graph data can be represented in a relational database—but the result is often awkward. That’s why we have different systems for different purposes, not a single one-size-fits-all solution.
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
requisite variety — the ability to survive under varied conditions. Often this means “fixing” input behind the scenes in code so that all the varied inputs conform to the format that the code/database needs
Dan Saffer (Microinteractions: Full Color Edition: Designing with Details)
Two or more things are orthogonal if changes in one do not affect any of the others. In a well-designed system, the database code will be orthogonal to the user interface: you can change the interface without affecting the database, and swap databases without changing the interface.
Andrew Hunt (The Pragmatic Programmer: From Journeyman to Master)
CRM (Customer Relationship Management) is a marketing strategy that focuses on managing interactions and relationships with customers. CRM enables businesses to improve customer satisfaction, loyalty, and retention by providing personalized experiences that meet their needs. CRM is an essential aspect of modern marketing as it enables businesses to understand their customers' behavior, preferences, and needs and develop targeted marketing campaigns that resonate with them. In Go High Level, CRM (Customer Relationship Management) is a core component of the platform. The CRM functionality in Go High Level enables businesses to manage their customer interactions and relationships more effectively, improving customer satisfaction, loyalty, and retention. The CRM functionality in Go High Level includes a range of features and tools designed to help businesses automate and streamline their customer-facing processes, as well as provide them with insights into their customers' behavior, preferences, and needs. In essence, CRM is a set of practices, technologies, and strategies that businesses use to manage their customer interactions and relationships. The goal of CRM is to build stronger, more meaningful relationships with customers by providing them with personalized experiences and tailored solutions. CRM in marketing can be divided into three main categories: operational CRM, analytical CRM, and collaborative CRM. Operational CRM focuses on automating and streamlining customer-facing processes, such as sales, marketing, and customer service. This type of CRM is designed to improve efficiency and productivity by automating repetitive tasks and providing a centralized database of customer information. Operational CRM includes features such as sales pipeline management, lead nurturing, and customer service management. Analytical CRM focuses on analyzing customer data to gain insights into their behavior, preferences, and needs. This type of CRM enables businesses to make data-driven decisions by providing them with a better understanding of their customers' needs and preferences. Analytical CRM includes features such as customer segmentation, data mining, and predictive analytics. Collaborative CRM focuses on enabling businesses to collaborate and share customer information across different departments and functions. This type of CRM helps to break down silos within organizations and improve communication and collaboration between different teams. Collaborative CRM includes features such as customer feedback management, social media monitoring, and knowledge management. CRM is important for marketing because it enables businesses to build stronger, more meaningful relationships with customers. By understanding their customers' behavior, preferences, and needs, businesses can develop targeted marketing campaigns that resonate with them. This results in higher customer satisfaction, loyalty, and retention. CRM can also help businesses to improve their sales and marketing processes by providing them with better visibility into their sales pipeline and enabling them to track and analyze their marketing campaigns' effectiveness. This enables businesses to make data-driven decisions to improve their sales and marketing strategies, resulting in increased revenue and growth. Another benefit of CRM in marketing is that it enables businesses to personalize their marketing campaigns. Personalization is essential in modern marketing as it enables businesses to tailor their marketing messages and solutions to meet their customers' specific needs and preferences. This results in higher engagement and conversion rates, as customers are more likely to respond to marketing messages that resonate with them. Lead Generation: Go High Level provides businesses with a range of tools to generate leads, including customizable landing pages, web forms, and social media integrations.
What is CRM in Marketing?
The fact that SQL is more limited in functionality gives the database much more room for automatic optimizations.
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
The overall objective of logical design is to achieve a design that’s (a) hardware independent, for obvious reasons; (b) operating system and DBMS independent, again for obvious reasons; and finally, and perhaps a little controversially, (c) application independent (in other words, we’re concerned primarily with what the data is, rather than with how it’s going to be used). Application independence in this sense is desirable for the very good reason that it’s normally—perhaps always—the case that not all uses to which the data will be put are known at design time; thus, we want a design that’ll be robust, in the sense that it won’t be invalidated by the advent of application requirements that weren’t foreseen at the time of the original design.
C.J. Date (Database Design and Relational Theory: Normal Forms and All That Jazz)
What design theory does is state in a precise way what certain aspects of common sense consist of. In my opinion, that’s the real achievement—or one of the real achievements, anyway—of the theory: It formalizes certain commonsense principles, thereby opening the door to the possibility of mechanizing those principles (that is, incorporating them into computerized design tools). Critics of the theory often miss this point; they claim, quite rightly, that the ideas are mostly just common sense, but they don’t seem to realize it’s a significant achievement to state what common sense means in a precise and formal way.
C.J. Date (Database Design and Relational Theory: Normal Forms and All That Jazz)
Document-oriented databases like MongoDB [9], RethinkDB [10], CouchDB [11], and Espresso [12] support this data model.
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
All software systems can be decomposed into two major elements: policy and details. The policy element embodies all the business rules and procedures. The policy is where the true value of the system lives. The details are those things that are necessary to enable humans, other systems, and programmers to communicate with the policy, but that do not impact the behavior of the policy at all. They include IO devices, databases, web systems, servers, frameworks, communication protocols, and so forth.
Robert C. Martin (Clean Architecture: A Craftsman's Guide to Software Structure and Design)
The database, the query language, and even the schema are technical details that have nothing to do with the business rules or the UI. They will change at rates, and for reasons, that are independent of other aspects of the system. Consequently, the architecture should separate them from the rest of the system so that they can be independently changed. Thus we find the system divided into decoupled horizontal layers—the UI, application-specific business rules, application-independent business rules, and the database, just to mention a few.
Robert C. Martin (Clean Architecture: A Craftsman's Guide to Software Structure and Design)
When you are vertically separating use cases from one another, you will run into this issue, and your temptation will be to couple the use cases because they have similar screen structures, or similar algorithms, or similar database queries and/or schemas. Be careful. Resist the temptation to commit the sin of knee-jerk elimination of duplication. Make sure the duplication is real. By the same token, when you are separating layers horizontally, you might notice that the data structure of a particular database record is very similar to the data structure of a particular screen view. You may be tempted to simply pass the database record up to the UI, rather than to create a view model that looks the same and copy the elements across. Be careful: This duplication is almost certainly accidental. Creating the separate view model is not a lot of effort, and it will help you keep the layers properly decoupled.
Robert C. Martin (Clean Architecture: A Craftsman's Guide to Software Structure and Design)
the message broker behaves like a leader database, and the consumer like a follower.
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
A data warehouse is an organized store of data from all over the organization, specially designed to help make management decisions. Data can be extracted from operational database to answer a particular set of queries. This data, combined with other data, can be rolled up to a consistent granularity and uploaded to a separate data store called the data warehouse. Therefore, the data warehouse is a simpler version of the operational data base, with the purpose of addressing reporting and decision-making needs only.
Anil Maheshwari (Data Analytics Made Accessible)
More recently, there has been growing interest in change data capture (CDC), which is the process of observing all data changes written to a database and extracting them in a form in which they can be replicated to other systems. CDC is especially interesting if changes are made available as a stream, immediately as they are written.
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
In a tiny company like mine, it’s up to the owner to invent the way the company operates and to design the systems that keep track of what is happening. Fortunately, I find this to be an interesting challenge. If I had wanted to build only furniture, I could have kept myself very busy, but the company would not have grown. Without a rational way to handle information, we would have descended into permanent chaos. Thinking about information is different from ordinary work. The challenge is to find good ways, using data, to describe what’s happening in the real world. It’s aligning the description of the company with the activities of the company. My job as boss is to monitor both of these and to continually modify the description to fit the reality. My employees can’t do it—they each work on their piece of the process. I’m the only one who sees everything. I decide what to keep track of, and how to do it. I have two information systems. First, there’s my subjective impressions of the state of the shop, the mood of the workers, the eagerness of the customers, drawn from my observations and conversations. The second is objective, actual data that lives in separate fiefdoms: the accounting system, in QuickBooks; the contract and productions system, in FileMaker; e-mails and customer folders sit on our server; AdWords data lives in the cloud. So do our shared Google Docs spreadsheets, which act as supplementary databases. There are also a bunch of Excel sheets, dating back to 1997, when I first computerized (twelve years after starting the company). None of these subsystems talk to one another. Information passes between them via the people who use it. I’m the only person in the company who knows how it all fits together.
Paul Downs (Boss Life: Surviving My Own Small Business)
if you have kept your frameworks at arm’s length, then you should be able to unit-test all those use cases without any of the frameworks in place. You shouldn’t need the web server running to run your tests. You shouldn’t need the database connected to run your tests. Your Entity objects should be plain old objects that have no dependencies on frameworks or databases or other complications. Your use case objects should coordinate your Entity objects. Finally, all of them together should be testable in situ, without any of the complications of frameworks.
Robert C. Martin (Clean Architecture: A Craftsman's Guide to Software Structure and Design)
Non-relational databases might be the right choice if: •Your application requires super-low latency. •Your data are unstructured, or you do not have any relational data. •You only need to serialize and deserialize data (JSON, XML, YAML, etc.). •You need to store a massive amount of data.
Alex Xu (System Design Interview – An insider's guide)
wide database should be the inequity and bias with which “terrorist” content is already discovered and determined by companies. By and large, Silicon Valley tech companies rely on the US government to define “terrorism,” and remove content from groups on the State Department’s list of foreign designated terrorist organizations (FTOs). While I’ve heard staffers from Facebook suggest that doing so is a legal obligation, numerous lawyers I consulted with disagree. Just as the leader of a designated foreign terrorist organization can publish an opinion piece in the New York Times—they can and they have—so too can they publish a page on Facebook.
Jillian York (Silicon Values: The Future of Free Speech Under Surveillance Capitalism)
This means that the UI and the database can be plugins to the business rules. It means that the source code of the business rules never mentions the UI or the database.
Robert C. Martin (Clean Architecture: A Craftsman's Guide to Software Structure and Design)
WHICH LINES DO YOU DRAW, AND WHEN DO YOU DRAW THEM? You draw lines between things that matter and things that don’t. The GUI doesn’t matter to the business rules, so there should be a line between them. The database doesn’t matter to the GUI, so there should be a line between them. The database doesn’t matter to the business rules, so there should be a line between them.
Robert C. Martin (Clean Architecture: A Craftsman's Guide to Software Structure and Design)
Some of us have even been convinced that the database is the embodiment of the business rules. But, as we shall see in another chapter, this idea is misguided. The database is a tool that the business rules can use indirectly. The business rules don’t need to know about the schema, or the query language, or any of the other details about the database. All the business rules need to know is that there is a set of functions that can be used to fetch or save data. This allows us to put the database behind an interface.
Robert C. Martin (Clean Architecture: A Craftsman's Guide to Software Structure and Design)
From Microsoft OneNote, Google Keep, and Apple Notes to Notion and Evernote, digital notes apps have four powerful characteristics that make them ideal for building a Second Brain. They are: Multimedia: Just like a paper notebook might contain drawings and sketches, quotes and ideas, and even a pasted photo or Post-it, a notes app can store a wide variety of different kinds of content in one place, so you never need to wonder where to put something. Informal: Notes are inherently messy, so there’s no need for perfect spelling or polished presentation. This makes it as easy and frictionless as possible to jot things down as soon as they occur to you, which is essential to allow nascent ideas to grow. Open-ended: Taking notes is a continuous process that never really ends, and you don’t always know where it might lead. Unlike more specialized kinds of software that are designed to produce a specific kind of output (such as slide decks, spreadsheets, graphics, or videos), notes are ideal for free-form exploration before you have a goal in mind. Action-oriented: Unlike a library or research database, personal notes don’t need to be comprehensive or precise. They are designed to help you quickly capture stray thoughts so you can remain focused on the task at hand.
Tiago Forte (Building a Second Brain: A Proven Method to Organize Your Digital Life and Unlock Your Creative Potential)
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John Stackhouse (Mass Disruption: Thirty Years on the Front Lines of a Media Revolution)
We can use both endpoints to get data from Microsoft Dynamics CRM 2015 databases. Although SOAP supports more methods, OData is easy to develop with. The following table shows the differences between OData and Modern SOAP: OData Modern SOAP Only supports CURD, Associate and Disassociate methods Supports all methods Can't access metadata Metadata can be accessed Returns 50 records per page Returns 5,000 records per page Schema names used for entities and fields while writing queries Logical names used while writing queries Easy to write and provides a better development experience
Mahender Pal (Microsoft Dynamics CRM 2015 Application Design)
Note that there is not a focus on eliminating failures. Systems without failures, although robust, become brittle and fragile. When failures occur, it is more likely that the teams responding will be unprepared, and this could dramatically increase the impact of the incident.
Laine Campbell (Database Reliability Engineering: Designing and Operating Resilient Database Systems)
Alex Scotti: “Adventures in Building Your Own Database,” at All Your Base, November 2015.
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
it would be good to include staleness measurements in the standard set of metrics for databases. Eventual consistency is a deliberately vague guarantee, but for operability it’s important to be able to quantify “eventual.
Martin Kleppmann (Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems)
Specific Architectural Topics Is the overall organization of the program clear, including a good architectural overview and justification? Are major building blocks well defined, including their areas of responsibility and their interfaces to other building blocks? Are all the functions listed in the requirements covered sensibly, by neither too many nor too few building blocks? Are the most critical classes described and justified? Is the data design described and justified? Is the database organization and content specified? Are all key business rules identified and their impact on the system described? Is a strategy for the user interface design described? Is the user interface modularized so that changes in it won’t affect the rest of the program? Is a strategy for handling I/O described and justified? Are resource-use estimates and a strategy for resource management described and justified for scarce resources like threads, database connections, handles, network bandwidth, and so on? Are the architecture’s security requirements described? Does the architecture set space and speed budgets for each class, subsystem, or functionality area? Does the architecture describe how scalability will be achieved? Does the architecture address interoperability? Is a strategy for internationalization/localization described? Is a coherent error-handling strategy provided? Is the approach to fault tolerance defined (if any is needed)? Has technical feasibility of all parts of the system been established? Is an approach to overengineering specified? Are necessary buy-vs.-build decisions included? Does the architecture describe how reused code will be made to conform to other architectural objectives? Is the architecture designed to accommodate likely changes? General Architectural Quality Does the architecture account for all the requirements? Is any part overarchitected or underarchitected? Are expectations in this area set out explicitly? Does the whole architecture hang together conceptually? Is the top-level design independent of the machine and language that will be used to implement it? Are the motivations for all major decisions provided? Are you, as a programmer who will implement the system, comfortable with the architecture?
Steve McConnell (Code Complete)
We knew that the existing split of file system and database would no longer work. We wanted a new, high-performance file system that supported the database functionality the BeOS was known for as well as a mechanism to support multiple file systems.
Dominic Giampaolo (Practical File System Design)
Working under the auspices of the Heritage Foundation, a fifty-year-old conservative think tank, they designed a four-pronged plan that included a detailed policy platform, a huge database of potential administration hires, training courses for aspiring staffers, and a playbook for a blitzkrieg takeover of the government on Day One. They named it Project 2025.
David A. Graham (The Project: How Project 2025 Is Reshaping America)
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