Data Videos
#Data -Introducing the Coco Framework
Monday, August 14, 2017
AWS Knowledge Center Videos: How do I encrypt my data in Amazon EFS?
Data Videos
#Data -AWS Knowledge Center Videos: How do I encrypt my data in Amazon EFS?
Finding Triples With Neo4j
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Finding Triples With Neo4j
A user had an interesting Neo4j question on Stack Overflow the other day:
I have two types of nodes in my graph. One type is Testplan and the other is Tag. Testplans are tagged to Tags. I want most common pairs of Tags that share the same Testplans with a Tag having a specific name. I have been able to achieve the most common Tags sharing the same Testplan with one Tag, but getting confused when trying to do it for pairs of Tags.Their Cypher query looked like this:
Syncing Databases Properly to Work With eCommerce Business Applications
DZone Database Zone
Syncing Databases Properly to Work With eCommerce Business Applications
When it comes to planning database backends for eCommerce sites and applications, one may come across many technical terms like:
Simple MySQL PostgreSQL-powered Cloud Redundant database Multi-zone NoSQL backendThese all are standard terms that describe databases of different eCommerce systems, but what do they mean and how do they work? What is the purpose of a database? Is it possible to run an eCommerce store without a database?
2 Approaches to Scalable Database Design
DZone Database Zone
2 Approaches to Scalable Database Design
Any form of application used for data analysis is stringently dependent on its ability to retrieve queries fast. However, when working with larger or more complex datasets, as well as an increasing amount of concurrent users, the performance depends largely on the underlying analytical database — whether this is built into the application as part of a single-stack tool or implemented via a separate data warehouse layer.
What Makes a Scalable Database?Database scalability is a concept in database design that emphasizes the capability of a database to handle growth in the amount of data and users. In the modern applications sphere, two types of workloads have emerged: analytical and transactional workloads. Planning for workload growth must take into account operating system, database design, and hardware design decisions.
Cypher: Write Fast and Furious
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Cypher: Write Fast and Furious
Editor’s Note: This presentation was given by Christophe Willemsen at GraphConnect San Francisco in October 2016.
Presentation SummaryIn this presentation, Christophe Willemsen covers a variety of do-and-don’t tips to help your Cypher queries run faster than ever in Neo4j.
Sunday, August 13, 2017
Syncing Databases Properly to Work With eCommerce Business Applications
DZone Database Zone
Syncing Databases Properly to Work With eCommerce Business Applications
When it comes to planning database backends for eCommerce sites and applications, one may come across many technical terms like:
Simple MySQL PostgreSQL-powered Cloud Redundant database Multi-zone NoSQL backendThese all are standard terms that describe databases of different eCommerce systems, but what do they mean and how do they work? What is the purpose of a database? Is it possible to run an eCommerce store without a database?
Saturday, August 12, 2017
Azure Functions With Couchbase Server
DZone Database Zone
Azure Functions With Couchbase Server
Azure Functions are Microsoft’s answer to Amazon’s Lambdas or Google’s Cloud Functions (AKA “serverless” architecture). They give you a way to deploy small pieces of code and let Azure handle the underlying server. I’ve never used them before, so I thought I would give them a try beyond “Hello, World” by getting them to work with Couchbase Server.
There are more options in Azure Functions beyond simple HTTP events (for example, blob triggers, GitHub webhooks, Azure Storage queue triggers, etc.). But, for this blog post, I’m going to focus on just HTTP events. I’ll create simple GET and SET endpoints that interact with Couchbase Server.
Friday, August 11, 2017
Faster PostgreSQL Counting
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Faster PostgreSQL Counting
Everybody counts — but not always quickly. This article takes a close look into how PostgreSQL optimizes counting. If you know the tricks, there are ways to count rows orders of magnitude faster than you do already.
The problem is actually under-described — there are several variations of counting, each with their own methods. First, think about whether you need an exact count or if an estimate suffices. Next, are you counting duplicates or just distinct values? Finally, do you want a lump count of an entire table or will you want to count only those rows matching extra criteria?
Scalable MySQL Cluster With ProxySQL and Orchestrator
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Scalable MySQL Cluster With ProxySQL and Orchestrator
MySQL is one of the most popular open-source relational databases, used by lots of projects around the world — including incredibly large-scale ones like Facebook, Twitter, and YouTube. Obviously, such projects need a truly reliable and highly available data storing system to ensure the appropriate level of a service quality. And the very first and the main way to get the most efficiency from your data storage is setting up database clustering so that it could process a big number of requests simultaneously and remain workable in conditions of increased load. However, configuring such solution from the scratch can appear to be a rather complicated task.
Thus, the Jelastic team has prepared a one-click installation package for you: a Scalable MySQL Cluster with out-of-box master-slave replication, event request distribution, and node auto-discovery. It is intended to instantly deploy a pair of interconnected MySQL containers, which handle asynchronous data replication and are automatically reconfigured upon cluster scaling (i.e. changing the number of nodes). In addition, this solution is supplied with a ProxySQL load balancer in front of the database nodes set and embedded Orchestrator for its convenient management via GUI.
Execute an Oracle Stored Procedure With Nested Table as a Parameter
DZone Database Zone
Execute an Oracle Stored Procedure With Nested Table as a Parameter
The objective of this tutorial is to demonstrate the steps required to execute an Oracle stored procedure with a nested table as one of the parameters from a Mule flow.
To demonstrate the application, I will be using a simple use case of inserting employee records by calling an Oracle stored procedure with a nested table as one of the parameters. Each employee record has two columns: the employee’s department and the nested table of a data structure with employee name and employee number as attributes.
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