A database is an organized collection of data, generally stored and accessed electronically from a computer system. Where databases are more complex they are often developed using formal design and modeling techniques.
Ruth  Nabimanya

Ruth Nabimanya


System Databases in SQL Server


In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

  • Master
  • Model
  • MSDB
  • Tempdb
  • Resource
  • This database contains all the System level Information in SQL Server. The Information in form of Meta data.
  • Because of this master database, we are able to access the SQL Server (On premise SQL Server)
  • This database is used as a template for new databases.
  • Whenever a new database is created, initially a copy of model database is what created as new database.
  • This database is where a service called SQL Server Agent stores its data.
  • SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.
  • The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
  • User can create their own version of temporary tables and those are stored in Tempdb.
  • But this database is destroyed and recreated every time when we restart the instance of SQL Server.
  • The resource database is a hidden, read only database that holds the definitions of all system objects.
  • When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database

System Databases in SQL Server
Siphiwe  Nair

Siphiwe Nair


SingleStore: The One Stop Shop For Everything Data

  • SingleStore works toward helping businesses embrace digital innovation by operationalising “all data through one platform for all the moments that matter”

The pandemic has brought a period of transformation across businesses globally, pushing data and analytics to the forefront of decision making. Starting from enabling advanced data-driven operations to creating intelligent workflows, enterprise leaders have been looking to transform every part of their organisation.

SingleStore is one of the leading companies in the world, offering a unified database to facilitate fast analytics for organisations looking to embrace diverse data and accelerate their innovations. It provides an SQL platform to help companies aggregate, manage, and use the vast trove of data distributed across silos in multiple clouds and on-premise environments.

**Your expertise needed! **Fill up our quick Survey

#featured #data analytics #data warehouse augmentation #database #database management #fast analytics #memsql #modern database #modernising data platforms #one stop shop for data #singlestore #singlestore data analytics #singlestore database #singlestore one stop shop for data #singlestore unified database #sql #sql database

SingleStore: The One Stop Shop For Everything Data
Ruth  Nabimanya

Ruth Nabimanya


How to Efficiently Choose the Right Database for Your Applications

Finding the right database solution for your application is not easy. Learn how to efficiently find a database for your applications.

Finding the right database solution for your application is not easy. At iQIYI, one of the largest online video sites in the world, we’re experienced in database selection across several fields: Online Transactional Processing (OLTP), Online Analytical Processing (OLAP), Hybrid Transaction/Analytical Processing (HTAP), SQL, and NoSQL.

Today, I’ll share with you:

  • What criteria to use for selecting a database.
  • What databases we use at iQIYI.
  • Some decision models to help you efficiently pick a database.
  • Tips for choosing your database.

I hope this post can help you easily find the right database for your applications.

#database architecture #database application #database choice #database management system #database management tool

How to Efficiently Choose the Right Database for Your Applications
Ruth  Nabimanya

Ruth Nabimanya


What is SQL? And Where is it Used?

Define: SQL [pron. “sequel”] – stands for Structured Query Language (SQL), used by databases to model and manage tabular/relational datasets; a set of standardized Data Definition Language (DDL) functions to create tables, views, and define relational schema models, and Data Manipulation Language (DML) to query, insert, and modify data in the tables.

*Read-only select queries are technically part of its Data Query Language (DQL) group. Still, operationally, many refer to it as DML because it can do more than read-only queries.

Super Brief History of SQL

Who Uses SQL?

What Can a SQL Database Do?

What is a SQL Query?

Different SQL Database Platforms


Application Developers

SQL for Big Data

Further Reading

#sql #database #relational-database #nosql #json #sql-database #database-administration #databases-best-practices

What is SQL? And Where is it Used?
Brain  Crist

Brain Crist


How to Build a Pokedex React App with a Slash GraphQL Backend

Frontend developers want interactions with the backends of their web applications to be as painless as possible. Requesting data from the database or making updates to records stored in the database should be simple so that frontend developer can focus on what they do best: creating beautiful and intuitive user interfaces.

GraphQL makes working with databases easy. Rather than relying on backend developers to create specific API endpoints that return pre-selected data fields when querying the database, frontend developers can make simple requests to the backend and retrieve the exact data that they need—no more, no less. This level of flexibility is one reason why GraphQL is so appealing.

Even better, you can use a _hosted _GraphQL backend—Slash GraphQL (by Dgraph). This service is brand new and was publicly released on September 10, 2020. With Slash GraphQL, I can create a new backend endpoint, specify the schema I want for my graph database, and—voila!—be up and running in just a few steps.

The beauty of a hosted backend is that you don’t need to manage your own backend infrastructure, create and manage your own database, or create API endpoints. All of that is taken care of for you.

In this article, we’re going to walk through some of the basic setup for Slash GraphQL and then take a look at how I built a Pokémon Pokédex app with React and Slash GraphQL in just a few hours!

#development #web developement #databases #graph databases #reactjs #database design #database architecture #pokemon #graph databases in the cloud #dgraph

How to Build a Pokedex React App with a Slash GraphQL Backend
Ruth  Nabimanya

Ruth Nabimanya


What Is a Smart Database Proxy?

You can change how your database systems behave without changing either clients or servers, by defining your logic in a smart database proxy.

Smart database proxies may not be familiar to many people, and it’s a shame because they can solve many difficult problems elegantly. This article explains what they are, what they do, and when they are useful.

A Quick Comparison

Why Would You Want to Do That?

Two Simple Examples

A Variety of Uses

What About Performance?

But That’s Not the Real Data!

A State of Mind

#database #database access #database application development #proxies #proxy server #database infrastructure

What Is a Smart Database Proxy?
Ruth  Nabimanya

Ruth Nabimanya


Which Database Is Right For You?Graph Database vs. Relational Database

At the very beginning of most development endeavors lies an important question: What database do I choose? There is such an abundance of database technologies at this moment, it’s no wonder many developers don’t have the time or energy to research new ones. If you are one of those developers and you aren’t very familiar with graph databases in general, you’ve come to the right place!

In this article, you will learn about the main differences between a graph database and a relational database, what kind of use-cases are best suited for each database type, and what are their strengths and weaknesses.

How Does a Graph Database Differ from a Relational Database?

The Graph Data Model

The Relational Data Model

When to use a Graph Database?

When not to use a Graph Database

Is a Graph Database Worth it?

#graph-database #relational-database #graph-theory #graph-analysis #data-analytics #networks #data #database

Which Database Is Right For You?Graph Database vs. Relational Database

Benchmarking the Mainstream Open Source Distributed Graph Databases

The deep learning and knowledge graph technologies have been developing rapidly in recent years. Compared with the “black box” of deep learning, knowledge graphs are highly interpretable, thus are widely adopted in such scenarios as search recommendations, intelligent customer support, and financial risk management.

Meituan has been digging deep in the connections buried in the huge amount of business data over the past few years and has gradually developed the knowledge graphs in nearly ten areas, including cuisine graphs, tourism graphs, and commodity graphs. The ultimate goal is to enhance the smart local life.

Compared with the traditional RDBMS, graph databases can store and query knowledge graphs more efficiently. It gains obvious performance advantage in multi-hop queries to select graph databases as the storage engine. Currently, there are dozens of graph database solutions out there on the market.

It is imperative for the Meituan team to select a graph database solution that can meet the business requirements and to use the solution as the basis of Meituan’s graph storage and graph learning platform. The team has outlined the basic requirements as below per our business status quo:

  1. It should be an open-source project which is also business-friendly

By having control over the source code, the Meituan team can ensure data security and service availability.

  1. It should support clustering and should be able to scale horizontally in terms of both storage and computation capabilities

The knowledge graph data size in Meituan can reach hundreds of billions of vertices and edges in total and the throughput can reach tens of thousands of QPS. With that being said, the single-node deployment cannot meet Meituan’s storage requirements.

  1. It should work under OLTP scenarios with the capability of multi-hop queries at the millisecond level.

To ensure the best search experience for Meituan users, the team has strictly restricted the timeout value within all chains of paths. Therefore, it is unacceptable to respond to a query at the second level.

  1. It should be able to import data in batch

The knowledge graph data is usually stored in data warehouses like Hive. The graph database should be equipped with the capability to quickly import data from such warehouses to the graph storage to ensure service effectiveness.

The Meituan team has tried the top 30 graph databases on DB-Engines and found that most well-known graph databases only support single-node deployment with their open-source edition, for example, Neo4j, ArangoDB, Virtuoso, TigerGraph, RedisGraph. This means that the storage service cannot scale horizontally and the requirement to store large-scale knowledge graph data cannot be met.

After thorough research and comparison, the team has selected the following graph databases for the final round: Nebula Graph (developed by a startup team who originally came from Alibaba), Dgraph (developed by a startup team who originally came from Google), and HugeGraph (developed by Baidu).

A Summary of The Testing Process

Hardware Configuration

  1. Database instances: Docker containers running on different machines
  2. Single instance resources: 32 Cores, 64 GB Memory, 1 TB SSD (Intel® Xeon® Gold 5218 CPU @ 2.30 GHz)
  3. Number of instances: Three

#database #tutorial #graph database #database performance #nebula graph #dgraph #graph database adoption

Benchmarking the Mainstream Open Source Distributed Graph Databases
Ruth  Nabimanya

Ruth Nabimanya


Document Databases vs Relational Databases (Podcast Transcript)

Javascript developers rejoice! Amy Tom talks to Eric Bishard and Arun Vijayaraghavan about the differences between a Document Database and a Relational Database. Eric and Arun explain JSON Document Databases and also discuss NodeJS SDKs, ODMs vs ORMs, and more. Eric is the Developer Advocate and Arun is a Product Manager; both work at Couchbase.

In this episode, Amy chats to Eric and Arun about:

The differences between a Document Database and a Relational Database (06:56)The differences between an ODM and an ORM (18:45)Learning Nickel Querying through Ottoman ODM (26:58)

Follow Arun Vijayaraghavan and Eric Bishard:

  • Follow Arun Vijayaraghavan on GitHub
  • Connect with Arun Vijayaraghavan on LinkedIn
  • Follow Eric Bishard on Twitter
  • Follow Eric Bishard on GitHub
  • Follow Eric Bishard on

Read more on Hacker Noon:

Eric Bishard’s Introduction to Ottoman on Hacker Noon


#hackernoon-podcast #podcast #tech-podcasts #json #database #document-database #javascript #database-administration

Document Databases vs Relational Databases (Podcast Transcript)
Cayla  Erdman

Cayla Erdman


AlaSQL in Action: The JavaScript SQL Database

What’s AlaSQL?

For starters, this open-source project garners 11k weekly downloads off npm and has over 5K stars on GitHub.

I was surprised to see that there aren’t more posts about this popular lightweight client-side in-memory SQL database online apart from this awesome article I found. However, AlaSQL’s website gets straight to the point:

  • JavaScript SQL database for browser and Node.js.
  • Handles both traditional relational tables and nested JSON data (NoSQL).
  • Export, store, and import data from localStorage, IndexedDB, or Excel.

What’s not to love? AlaSQL was designed to work in browser and Node.js, is fast, and super easy to use. Some say that when you need to find data fast and want a better alternative to a full database or Redis, check out AlaSQL.

We Love AlaSQL, too!

HarperDB has had the pleasure of using AlaSQL for its backend SQL functionality. We chose to use AlaSQL because it has extensive language support, is well supported and extensible, can execute SQL against data sets (JSON or Arrays), and the ability to generate reusable functions. AlaSQL enables us to convert SQL language into an Abstract Syntax Tree (AST) that we can programmatically interpret into our data model. We can feed data into a processor that can perform the calculations native to SQL, using the functions defined in the library throughout our project as APIs and not just in the context of a SQL call.

AlaSQL is extensible and allows us to create custom functions. For example, we created a custom function called SEARCH_JSON that allows HarperDB users to search and transform nested documents. This function wraps a popular npm package called JSONata. With AlaSQL we were able to embed another open-source package as a simple function call. Our implementation was as easy as defining the function (Note this is sample code):



const jsonata = require('jsonata');




 * wrapper function that implements the JSONata library, which performs searches, transforms, etc... on JSON


 * @param {String} jsonata_expression - the JSONata expression to execute


 * @param {any} data - data which will be evaluated


 * @returns {any}




function searchJSON(jsonata_expression, data){


    if(typeof jsonata_expression !== 'string' || jsonata_expression.length === 0){


        throw new Error('search json expression must be a non-empty string');




    let alias = '__' + jsonata_expression + '__';




        this.__ala__.res = {};




    if(hdb_utils.isEmpty(this.__ala__.res[alias])) {


        let expression = jsonata(jsonata_expression);


        this.__ala__.res[alias] = expression;




    return this.__ala__.res[alias].evaluate(data);




//Then define a custom function in AlaSQL:


const alasql = require('alasql');


alasql.fn.search_json = alasql.fn.SEARCH_JSON = searchJSON;

We are happy with our decision to use AlaSQL to interpret SQL into our data model and run performant queries against as much SQL as possible. That’s why we hosted the creators of AlaSQL on June 16th for a showcase to get an inside look at how AlaSQL was created, how it grew in popularity, and real-world use cases and products. It was awesome to hear from AlaSQL creators Mathias Rangel Wulff and Andrey Gershun. After Q&A on AlaSQL, our CTO Kyle Bernhardy shared more about using AlaSQL as HarperDB’s engine to interpret and parse complex SQL into our data model and perform simple to complex SQL CRUD operations, as well as exposing other libraries like Turf.js, JSONata and more.

Looking for more resources on this innovative client-side in-memory SQL database? Check out this JavaScript library designed for:

  • Fast in-memory SQL data processing for BI and ERP applications on fat clients
  • Easy ETL and options for persistence by data import/manipulation/export of several formats
  • All major browsers, Node.js, and mobile applications

#database #sql #databases #sql (structured query language) #database applications #code activation #sql aggregation

AlaSQL in Action: The JavaScript SQL Database
Ruth  Nabimanya

Ruth Nabimanya


A Comprehensive Guide To Database Architectures And Use Cases

With over 300 databases on the market, how do you determine which is right for your specific use case or skill set?

We continue to see the common debate of SQL vs. NoSQL and other database comparisons all over social media and platforms like Hackernoon. In most cases, it’s not that one database is better than the other, it’s that one is a better fit for a specific use case due to numerous factors.

Last year, our CTO Kyle Bernhardy, led an awesome talk titled A Deep Dive Into Database Architectures. You can watch this talk at the link, but since this is such a prominent discussion topic we thought it might be helpful to summarize. This article will provide an overview on database architectures, including use cases and pros & cons for each of them.

Let’s start with general considerations when selecting a database. It’s important to understand things such as data type / structure, data volume, consistency, write & read frequency, hosting, cost, security, and integration constraints. The more you know about these factors, the easier it will be to pick the right database for your project.

You may already know that there are generally 3 database hosting options:


  • Database fully maintained by organization on servers running within their data center(s)
  • More control, but usually more expensive and time consuming

Cloud Hosted

  • Servers are maintained by cloud providers, organizations maintain database software and operating system running on the machine
  • Flexible scaling and no server upkeep, but no control over physical server and potential network limitations

Database-as-a-Service (DBaaS)

  • Database maintained by service provider, organizations only charged for usage of service
  • Cost effective and zero upkeep, but data stewardship and potential network limitations

Now for the part you’ve been waiting for - database architectures.

#database #databases #database-architecture #sql #nosql #software-development #dbaas #cloud-computing

A Comprehensive Guide To Database Architectures And Use Cases
Kaia  Schmitt

Kaia Schmitt


How to Insert Records in SQLite Database Python | Python Built-In Database - IV

Insert Records - Python Built-In Database - SQLite.

Github -

#database #python #sqlite database python #sqlite #database python

How to Insert Records in SQLite Database Python | Python Built-In Database - IV
Kaia  Schmitt

Kaia Schmitt


How to Create Table in SQLite Database Python | Python Built-In Database - III

Create Table - Python Built-In Database - SQLite.

Github -

#sqlite #database #python #sqlite database python #database python

How to Create Table in SQLite Database Python | Python Built-In Database - III
Kaia  Schmitt

Kaia Schmitt


How to Use Select Query in SQLite Database Python | Python Built-In Database - V

Querying Data - Python Built-In Database - SQLite.

Github -

#python #database #sqlite database python #query #sqlite #sqlite database

How to Use Select Query in SQLite Database Python | Python Built-In Database - V
Ruth  Nabimanya

Ruth Nabimanya


List of Available Database for Current User In SQL Server


When working in the SQL Server, we may have to check some other databases other than the current one which we are working. In that scenario we may not be sure that does we have access to those Databases?. In this article we discuss the list of databases that are available for the current logged user in SQL Server

Get the list of database

#sql server #available databases for current user #check database has access #list of available database #sql #sql query #sql server database #sql tips #sql tips and tricks #tips

List of Available Database for Current User In SQL Server