Loma  Baumbach

Loma Baumbach

1597104240

9 offbeat databases worth a look

By and large, if you need a database, you can reach for one of the big names—MySQL/MariaDBPostgreSQLSQLiteMongoDB—and get to work. But sometimes the one-size-fits-all approach doesn’t fit all. Every now and then your use case falls down between barstools, and you need to reach for something more specialized. Here are nine offbeat databases that run the gamut from in-memory analytics to key-value stores and time-series systems.

TABLE OF CONTENTS

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DuckDB

The phrase “SQL OLAP system” generally conjures images of data-crunching monoliths or sprawling data warehouse clusters. DuckDB is to analytical databases what SQLlite is to MySQL and PostgreSQL. It isn’t designed to run at the same scale as full-blown OLAP solutions, but to provide fast, in-memory analytical processing for local datasets.

InfoWorld’s 2020 Technology of the Year Award winners: The best software development, cloud computing, data analytics, and machine learning products of the year ]

Many of DuckDB’s features are counterparts to what’s found in bigger OLAP products, even if smaller in scale. Data is stored as columns rather than rows, and query processing is vectorized to make the best use of CPU caching. You won’t find much in the way of native connectivity to reporting solutions like Tableau, but it shouldn’t be difficult to roll such a solution manually. Aside from bindings for C++, DuckDB also connects natively to two of the most common programming environments for analytics, Python and R.

EdgeDB

“Edge” is a term used in graph databases to refer to the connection or relationship between two entities or nodes (such as between a customer and an order, or between an order and a product, etc.) of a highly connected dataset. EdgeDB uses the PostgreSQL core and all the properties it provides (like ACID transactions and industrial-strength reliability) to build what its makers call an “object-relational database” with strong field types and a SQL-like query language.

Thus EdgeDB combines NoSQL-like ease of use and immediacy, the relational modeling power of a graph database, and the guarantees and consistency of SQL. Even though EdgeDB is not formally a document database, you can use it to store data that way. And you can use the GraphQL query language to easily retrieve data from EdgeDB, just as you can with native graph databases such as Neo4j.

FoundationDB

An open source project spearheaded by Apple, FoundationDB is a “multi-model” database that stores data internally as key-value pairs (essentially the NoSQL model), but can be organized into relational tables, graphs, documents, and many other data structures. ACID transactions guarantee data integrity, and horizontal scaling and replication are both available out of the box. FoundationDB’s design comes with some stiff restrictions, though: keys, values, and transactions all have hard size limits, and transactions have hard time limits as well.

HarperDB

The goal behind HarperDB is to provide a single database for handling structured and unstructured data in an enterprise—somewhere between a multi-model database like FoundationDB and a data warehouse or OLAP solution. Ingested data is deduplicated and made available for queries through the interface of your choice: SQL, NoSQL, Excel, etc. BI solutions like Tableau or Power BI can integrate directly with HarperDB without the data needing to be extracted or processed. Both enterprise and community editions are available.

RECOMMENDED WHITEPAPERS
Also on InfoWorld: How to choose the right database for your application ]

KeyDB

As popular and powerful as Redis is, the in-memory key-value store has been criticized for falling short in threaded performance and ease of use. KeyDB is protocol-compatible with Redis, so can be used as a drop-in replacement. But KeyDB adds some nifty under-the-hood improvements, chiefly multithreading for network I/O operations and query parsing. Plans for the next edition of Redis, Redis 6, include threaded I/O as well, but KeyDB is available now.

M3DB

A product of Uber’s internal engineering team, M3DB is a distributed time-series database that is used in Uber’s metrics platform (essentially as a data store for Prometheus). Borrowing ideas from Apache Cassandra and a Facebook project named “Gorilla,” M3DB allows arbitrary time precision, out-of-order insertions, and configurable levels of replication and read consistency. However, the creators note that M3DB might not be suitable for all time-series database use cases. For instance, M3DB can’t insert data out of order beyond a given time window (the default is two hours), and it is mainly optimized for storing and retrieving 64-bit floats rather than other kinds of data.

#database

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9 offbeat databases worth a look
Ruth  Nabimanya

Ruth Nabimanya

1620633584

System Databases in SQL Server

Introduction

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
Master
  • 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)
Model
  • 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.
MSDB
  • 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.
TempDB
  • 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.
Resource
  • 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

Siphiwe  Nair

Siphiwe Nair

1625133780

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

Ruth  Nabimanya

Ruth Nabimanya

1620640920

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

Loma  Baumbach

Loma Baumbach

1597104240

9 offbeat databases worth a look

By and large, if you need a database, you can reach for one of the big names—MySQL/MariaDBPostgreSQLSQLiteMongoDB—and get to work. But sometimes the one-size-fits-all approach doesn’t fit all. Every now and then your use case falls down between barstools, and you need to reach for something more specialized. Here are nine offbeat databases that run the gamut from in-memory analytics to key-value stores and time-series systems.

TABLE OF CONTENTS

SHOW MORE

DuckDB

The phrase “SQL OLAP system” generally conjures images of data-crunching monoliths or sprawling data warehouse clusters. DuckDB is to analytical databases what SQLlite is to MySQL and PostgreSQL. It isn’t designed to run at the same scale as full-blown OLAP solutions, but to provide fast, in-memory analytical processing for local datasets.

InfoWorld’s 2020 Technology of the Year Award winners: The best software development, cloud computing, data analytics, and machine learning products of the year ]

Many of DuckDB’s features are counterparts to what’s found in bigger OLAP products, even if smaller in scale. Data is stored as columns rather than rows, and query processing is vectorized to make the best use of CPU caching. You won’t find much in the way of native connectivity to reporting solutions like Tableau, but it shouldn’t be difficult to roll such a solution manually. Aside from bindings for C++, DuckDB also connects natively to two of the most common programming environments for analytics, Python and R.

EdgeDB

“Edge” is a term used in graph databases to refer to the connection or relationship between two entities or nodes (such as between a customer and an order, or between an order and a product, etc.) of a highly connected dataset. EdgeDB uses the PostgreSQL core and all the properties it provides (like ACID transactions and industrial-strength reliability) to build what its makers call an “object-relational database” with strong field types and a SQL-like query language.

Thus EdgeDB combines NoSQL-like ease of use and immediacy, the relational modeling power of a graph database, and the guarantees and consistency of SQL. Even though EdgeDB is not formally a document database, you can use it to store data that way. And you can use the GraphQL query language to easily retrieve data from EdgeDB, just as you can with native graph databases such as Neo4j.

FoundationDB

An open source project spearheaded by Apple, FoundationDB is a “multi-model” database that stores data internally as key-value pairs (essentially the NoSQL model), but can be organized into relational tables, graphs, documents, and many other data structures. ACID transactions guarantee data integrity, and horizontal scaling and replication are both available out of the box. FoundationDB’s design comes with some stiff restrictions, though: keys, values, and transactions all have hard size limits, and transactions have hard time limits as well.

HarperDB

The goal behind HarperDB is to provide a single database for handling structured and unstructured data in an enterprise—somewhere between a multi-model database like FoundationDB and a data warehouse or OLAP solution. Ingested data is deduplicated and made available for queries through the interface of your choice: SQL, NoSQL, Excel, etc. BI solutions like Tableau or Power BI can integrate directly with HarperDB without the data needing to be extracted or processed. Both enterprise and community editions are available.

RECOMMENDED WHITEPAPERS
Also on InfoWorld: How to choose the right database for your application ]

KeyDB

As popular and powerful as Redis is, the in-memory key-value store has been criticized for falling short in threaded performance and ease of use. KeyDB is protocol-compatible with Redis, so can be used as a drop-in replacement. But KeyDB adds some nifty under-the-hood improvements, chiefly multithreading for network I/O operations and query parsing. Plans for the next edition of Redis, Redis 6, include threaded I/O as well, but KeyDB is available now.

M3DB

A product of Uber’s internal engineering team, M3DB is a distributed time-series database that is used in Uber’s metrics platform (essentially as a data store for Prometheus). Borrowing ideas from Apache Cassandra and a Facebook project named “Gorilla,” M3DB allows arbitrary time precision, out-of-order insertions, and configurable levels of replication and read consistency. However, the creators note that M3DB might not be suitable for all time-series database use cases. For instance, M3DB can’t insert data out of order beyond a given time window (the default is two hours), and it is mainly optimized for storing and retrieving 64-bit floats rather than other kinds of data.

#database

Brain  Crist

Brain Crist

1600275600

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