Database Table Quotes

Nobody ever imagined a bunch of Orcs would steal a database table…

DBAs who rarely genuinely restructure schemas build an increasingly fossilized world, with byzantine grouping and bunching strategies. When DBAs don’t restructure the database, they’re not preserving a precious enterprise resource, they’re instead creating the concretized remains of every version of the schema, all overlaid upon one another via join tables.

If having a coffee in the morning doesn't wake you up, try deleting a table in a production database instead.

1.4.3. Each service has its own database A key characteristic of the microservice architecture is that the services are loosely coupled and communicate only via APIs. One way to achieve loose coupling is by each service having its own datastore. In the online store, for example, Order Service has a database that includes the ORDERS table, and Customer Service has its database, which includes the CUSTOMERS table. At development time, developers can change a service’s schema without having to coordinate with developers working on other services. At runtime, the services are isolated from each other—for example, one service will never be blocked because another service holds a database lock.

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

Database means a tables collected different information, so one site is a result of a collected tables????

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.

When two or more of their lighthuggers met, they would compare and update their respective nomenclature tables. If the first ship had assigned names to a group of worlds and their associated geographical features, and the second ship had no current entries for those bodies, it was usual for the second ship to amend its database with the new names. They might be flagged as provisional, unless a third ship confirmed that they were still unallocated.

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.

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.

retrieve?� When it comes to databases, chances are you’ll need to retrieve your data as often than you’ll need to insert it. That’s where this chapter comes in: you’ll meet the powerful SELECT statement and learn how to gain access to that important information you’ve been putting in your tables. You’ll even learn how to use WHERE, AND, and OR to selectively get to your data and even avoid displaying the data that you don’t need. I’m a star! Date or no date? 54 A better SELECT 57 What the * is that? 58 How to query your data types 64 More punctuation problems 65 Unmatched single quotes 66 Single quotes are special characters 67 INSERT data with single quotes in it 68 SELECT specific columns to limit results 73 SELECT specific columns for faster results 73 Combining your queries 80 Finding numeric values 83 Smooth Comparison Operators

To make sure your strings go from PHP to MySQL as UTF-8, make sure your database and tables are all set to the utf8mb4 character set and collation, and that you use the utf8mb4 character set in the PDO connection string.

The key is to break up the deployment into phases. Instead of adding, changing, and removing stuff—such as database columns and tables, constraints, services—all at once, add the new items early, with ways to ensure forward compatibility for the old version of the code. Later, after the release is rolled out, remove stuff that is no longer referenced, and add any new constraints that would have broken the old version.

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.

FOR c1 IN (SELECT OWNER,table_name, constraint_name FROM dba_constraints WHERE constraint_type = 'R' and owner=upper('&shema_name')) LOOP EXECUTE IMMEDIATE 'ALTER TABLE '||' "'||c1.owner||'"."'||c1.table_name||'" DROP CONSTRAINT ' || c1.constraint_name; END LOOP; FOR c1 IN (SELECT owner,object_name,object_type FROM dba_objects where owner=upper('&shema_name')) LOOP BEGIN IF c1.object_type = 'TYPE' THEN EXECUTE IMMEDIATE 'DROP '||c1.object_type||' "'||c1.owner||'"."'||c1.object_name||'" FORCE'; END IF; IF c1.object_type != 'DATABASE LINK' THEN EXECUTE IMMEDIATE 'DROP '||c1.object_type||' "'||c1.owner||'"."'||c1.object_name||'"'; END IF; EXCEPTION WHEN OTHERS THEN NULL; END;

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

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.

I define that the tech industry switches in all directions contrary to what people believe as the norm for the new Metaverse. Why spend trillions of dollars on big data when it is becoming more useless? We need dynamic content to create a boom in the tech industry for the next millennium. Why hire someone with a 4 year college degree for a career in database administration when companies can't afford to pay 100k a year? We can manage information stores perfectly fine with google sheets or microsoft excel. I thought that utilizing AI would completely switch off problematics in relationship to Data As A Service when programs are dynamically building hash tables for objects in random access memory, storing them as blockchains Inna virtualized file container ;)." - Jonathan Roy Mckinney

SELECT constraint_name, constraint_type, search_condition FROM user_constraints WHERE  table_name = 'AIRCRAFT_TYPES

Databases of this type are interrogated in a language called SQL. You send them commands like the one shown here to interact with their stored information. Understanding how to manipulate these databases is subtle. The example command, for example, creates a “view”: a virtual database table that pulls together data from multiple existing tables, and that can then be addressed by the SQL commands like a standard table. When to create views and how to do so well is a tricky question, one of many that you must understand and master to tease reasonable results out of real-world databases.

There are two main databases, the World Bank’s PovcalNet database and the World Inequality Database. Out of the many potential measures of inequality we initially select four, (but they all tend in the same directions), to wit (i) the Gini coefficient,3 Diagram 7.1, for a number of OECD countries, (ii) for a few selected countries the share of income of the top 10 and 1% of the distribution, Tables 7.1 and 7.2, and (iii) the share of wealth for the top 10%, Table 7.3. We show these latter data points for the income inequality data at five-year intervals for the USA, UK, Germany, France, Sweden, Italy Japan, China, Brazil, Egypt and India, and the wealth inequality data for four countries, USA, China, France and UK.

...note that relational systems require only that the database be perceived by the user as tables. Tables are the logical structure in a relational system, not the physical structure. At the physical level, in fact, the system is free to store the data any way it likes—using sequential files, indexing, hashing, pointer chains, compression, and so on—provided only that it can map that stored representation to tables at the logical level. Another way of saying the same thing is that tables represent an abstraction of the way the data is physically stored—an abstraction in which numerous storage level details (such as stored record placement, stored record sequence, stored data value representations, stored record prefixes, stored access structures such as indexes, and so forth) are all hidden from the user. ... The Information Principle: The entire information content of the database is represented in one and only one way—namely, as explicit values in column positions in rows in tables. This method of representation is the only method available (at the logical level, that is) in a relational system. In particular, there are no pointers connecting one table to another.

Data is compressed by different compression techniques (e.g. dictionary encoding, run length encoding, sparse encoding, cluster encoding, indirect encoding) in SAP HANA Column store. When main memory limit is reached in SAP HANA, the whole database objects (table, view,etc.) that are not used will be unloaded from the main memory and saved into the disk.

Blockchain can be described as “a “write only” digital platform that records and verifies transactions”. Simply put, blockchain is the next step within database development. It can also be called Distributed Ledger Technology (DLT). As a distributed ledger, it is designed and built with the goal of securely storing millions of data within its platform, leveraging a series of architecture tweaks for it. A blockchain is then, nothing else than a database made up of rows, columns, and tables. What differentiates it from other databases, is its sophisticated encryption, that makes it safer, transparent, and more trustworthy. Blockchain technology uses cryptography and digital signatures to prove identity, authenticity, and enforce read/write access rights. All transactions within a block which is part of the blockchain are visible; so there is full transparency for every transaction. What is more, once an entry goes into a blockchain ledger, it cannot be (easily) altered or erased. There is no “central power” overseeing the ledger of transactions. Instead, blockchain technology enables a decentralised and distributed ledger where transactions are shared among a network of computers—in almost real time—rather than being stored on a central server with a central authority (like a bank) overseeing transactions. A blockchain platform is formed out of infinite blocks. The system was inspired by the way bitcoin was first designed when invented in 2008. Similarly to bitcoin, every time a block is made, it will attach itself to the blockchain carrying along a “hash”, or fingerprint from the previous block. These have an important function as they use cryptography to authenticate the source of the transaction.