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Displaying posts with tag: analytics (reset)
Database Performance Webinar: Tired of MySQL Making You Wait?

Too often developers and DBAs struggle to pinpoint the root cause of MySQL database performance issues, and then spend too much time in trying to fix them. Wouldn’t it be great to bypass wasted guesswork and get right to the issue?

In our upcoming webinar Tired of MySQL Making You Wait? we’re going to help you discover how to significantly increase the performance of your applications and reduce database response time.

In this webinar, Principal Architect Alexander Rubin and Database Evangelist Janis Griffin will provide the key steps needed to identify, prioritize, and improve query performance.

They will discuss the following topics:

  • Wait time analytics using …
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Using JSON’s Arrays for MariaDB Dynamic Columns

The JSON format includes the concept of array. A JSON object cant contain an attribute of array type. We have seen that we can use the MariaDB CONNECT Storage Engine provided UDFs (user defined functions) to implement dynamic columns. Let us create a table with a text column containing a a JSON string and let ...continue reading "Using JSON’s Arrays for MariaDB Dynamic Columns"

On Hadoop RDBMS. Interview with Monte Zweben.

“HBase and Hadoop are the only technologies proven to scale to dozens of petabytes on commodity servers, currently being used by companies such as Facebook, Twitter, Adobe and Salesforce.com.”–Monte Zweben.

Is it possible to turn Hadoop into a RDBMS? On this topic, I have interviewed Monte Zweben, Co-Founder and Chief Executive Officer of Splice Machine.

RVZ

Q1. What are the main challenges of applications and operational analytics that support real-time, interactive queries on data updated in real-time for Big Data?

Monte Zweben: Let’s break down “real-time, interactive queries on data updated in real-time for Big Data”. “Real-time, interactive queries” means that results need to be returned in milliseconds to a few seconds.
For “Data updated in real-time” to happen, …

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Data Warehouse in the Cloud - How to Upload MySQL data into Amazon Redshift for reporting and analytics

October 27, 2014 By Severalnines

The term data warehousing often brings to mind things like large complex projects, big businesses, proprietary hardware and expensive software licenses. With Hadoop came open source data analysis software that ran on commodity hardware, this helped address at least some of the cost aspects. We had previously blogged about MongoDB and MySQL to Hadoop. But setting up and maintaining a Hadoop infrastructure might still be out of reach for small businesses or small projects with limited budgets. Well, perhaps then you might want to have a look at Redshift.

…

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Resources for Database Clusters: Performance Tuning for HAProxy, Support for MariaDB 10, Technical Blogs & More

August 28, 2014 By Severalnines Check Out Our Latest Resources for MySQL, MariaDB & MongoDB Clusters

 

Here is a summary of resources & tools that we’ve made available to you in the past weeks. If you have any questions on these, feel free to contact us!

 

New Technical Webinars

 

Performance Tuning of HAProxy for Database Load Balancing

09 September 2014 - with Baptiste Assmann of HAProxy Technologies

Do you know what HAProxy can tell you about your application and database instances? Do you know the difference between …

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Making Real-Time Analytics a Reality — TDWI -The Data Warehousing Institute

My article on how to make the real-time processing of information from traditional transactional stores into Hadoop a reality has been published over at TDWI:

Making Real-Time Analytics a Reality — TDWI -The Data Warehousing Institute.


Filed under: Articles Tagged: analytics, big data, data migration, databases, hadoop, mysql, …

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Big Data Integration & ETL - Moving Live Clickstream Data from MongoDB to Hadoop for Analytics

June 16, 2014 By Severalnines

MongoDB is great at storing clickstream data, but using it to analyze millions of documents can be challenging. Hadoop provides a way of processing and analyzing data at large scale. Since it is a parallel system, workloads can be split on multiple nodes and computations on large datasets can be done in relatively short timeframes. MongoDB data can be moved into Hadoop using ETL tools like Talend or Pentaho Data Integration (Kettle).

 

In this blog, we’ll show you how to integrate your MongoDB and Hadoop datastores using Talend. We have a MongoDB database collecting clickstream data from several websites. We’ll create a job in Talend to extract the documents from MongoDB, transform and then load them into HDFS. We will also show you how to schedule this job to be executed every 5 minutes.

 

Test Case

 

We have an application …

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Archival and Analytics - Importing MySQL data into Hadoop Cluster using Sqoop

May 16, 2014 By Severalnines

We won’t bore you with buzzwords like volume, velocity and variety. This post is for MySQL users who want to get their hands dirty with Hadoop, so roll up your sleeves and prepare for work. Why would you ever want to move MySQL data into Hadoop? One good reason is archival and analytics. You might not want to delete old data, but rather move it into Hadoop and make it available for further analysis at a later stage. 

 

In this post, we are going to deploy a Hadoop Cluster and export data in bulk from a Galera Cluster using Apache Sqoop. Sqoop is a well-proven approach for bulk data loading from a relational database into Hadoop File System. There is also Hadoop Applier available from …

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MariaDB CONNECT Storage Engine replay & slides available

The slides and replay of yesterday’s webinar on the MariaDB CONNECT storage engine have just been posted. First I want to thank the numerous attendees. You have shown great interest on the parallel execution of query on distributed MySQL Servers. I agree this is cool. The ODBC capabilities seems also to generate interest. This make [...]

MariaDB CONNECT Storage Engine and parallelism

The CONNECT Storage engine implement the concept of a table made of multiple tables. These underlying tables can be distributed remotely. For example the underlying remote tables can be of ODBC or MySQL table type. this allows to execute distributed queries. What is nice is that we can execute this distributed query with parallelism.

How [...]