Welcome to Xplenty's Blog

All things data

Xplenty Asana Integration

Xplenty Asana Integration

Xplenty, swoops in as a superhero in the world of ETL, to save you time (which means money) getting all that data into your company’s analytic data store. Grab data from Asana, as well as your other data sources, and transform and manipulate the data before it gets into your data warehouse.

Xplenty HipChat Integration

Xplenty HipChat Integration

Xplenty integration with HipChat is super fast and incredibly easy using Xplenty’s HipChat hooks. Xplenty’s HipChat hooks allow you to get real-time updates about your account activity into your HipChat rooms.

Xplenty Salesforce Integration

Xplenty Salesforce Integration

Xplenty's integration with Salesforce is the easiest way to read and process your data from Salesforce and move it to an analytic data stores such as Amazon Redshift or Google BigQuery.

Xplenty PagerDuty Integration

Xplenty PagerDuty Integration

We already integrate with many tools to reduce the bulk of time you likely spend on cleaning your data and preparing it for analytics. We want to continue to make ETL easy no matter what tools and platforms you use so we are constantly integrating with all the latest and greatest.

Our latest integration... is PagerDuty. And we are hooked!

Xplenty Slack Integration

Xplenty Slack Integration

At Xplenty, we aim to make moving and processing data as quick and seamless as possible.
We integrate with a ton of tools your teams already uses to make the process even easier, like Intercom, Hubspot and tons of others.

And today we wanted to share our latest integration. Slack!

Xplenty and Segment: Processing Your Customer Behavior Data

Xplenty and Segment: Processing Your Customer Behavior Data

With Segment and Xplenty, you can sort, filter and aggregate your customer behavior data without writing any code, or installing or maintaining anything.

How to Collect, Transform, and Visualize Your Data on the Cloud

How to Collect, Transform, and Visualize Your Data on the Cloud

Data collection, data processing, data visualization, and reporting are becoming more and more challenging as data volumes grow and data formats change. Combined with the increased use of public cloud infrastructure, developers and data professionals need to find new strategies and adopt new technologies to design and maintain scalable and cost-effective data architectures.

Top 3 Updates from AWS re:Invent 2014

Top 3 Updates from AWS re:Invent 2014

Amazon had some great news at AWS re:Invent 2014 in Vegas—and we were there to hear it. Most of them were geared toward enterprises and developers, but three stood out for us more than the rest.

Our Top 5 Picks for AWS re:Invent Sessions on Big Data

Our Top 5 Picks for AWS re:Invent Sessions on Big Data

Next week we’ll be heading down to Vegas for AWS re:Invent 2014. We’re eager to meet the Big Data community and attend AWS sessions. There are quite a few of them—227 to be exact—so here are our top five recommendations.

Is Hadoop Secure for the Enterprise?

Is Hadoop Secure for the Enterprise?

While Hadoop has proved its power for scalable storage and processing of Big Data, it may not be enterprise-ready when it comes to security. Hortonworks, Cloudera and MapR address this problem by providing Enterprise Hadoop distributions, and there are several Hadoop security projects, such as Apache Argus and Knox. But what does Hadoop provide right out of the box?

Hadoop YARN Turns One: Spark Killed the MapReduce Star

Hadoop YARN Turns One: Spark Killed the MapReduce Star

Hadoop YARN may be the gun that hangs on the wall in the first act and kills MapReduce in the last—Google Trends clearly shows that interest in Hadoop is still on the rise, but Apache Spark is closing in fast.

Hadoop YARN Turns One: Hadoop Renaissance

Hadoop YARN Turns One: Hadoop Renaissance

These days, there is a renaissance of Hadoop-based Big Data projects: Impala, Spark, Storm, Flink and HBase as well as several SQL-on-Hadoop tools. Most of these projects are still in their infancy though, if not in the Apache Incubator, so they’re mostly used by early adopters and none of them has become an industry standard. Yet.

Hadoop YARN Turns One: Upgrading to YARN

Hadoop YARN Turns One: Upgrading to YARN

YARN's promise was big and exciting, but did it deliver? Now that we’ve spent a year with the baby elephant, we're happy to announce YARN Week: a three post series about our YARN thoughts and experiences.

How to Prepare IBM SoftLayer Data for Analysis

How to Prepare IBM SoftLayer Data for Analysis

IBM joined the cloud computing market when it bought SoftLayer in 2013. One of the services that it provides is cloud-based storage, just like Amazon S3 and Rackspace. You can use it to store gigabytes or even terabytes of data, but at some point, you will need to analyze that data.

Processing Data from Rackspace Cloud Files

Processing Data from Rackspace Cloud Files

Amazon is not alone in the cloud: Rackspace, a cloud computing company that was established back in 1998 in Texas, is one of its major competitors. It also founded OpenStack, the open-source operating system for the cloud. So you may have gigabytes or even terabytes of data stored away on Rackspace, but what are you going to do with them?

Transform Data from Amazon RDS with Xplenty

Transform Data from Amazon RDS with Xplenty

How do you integrate data from Amazon RDS (Relational Database Service) with data from other sources such as S3, Redshift, or even MongoDB? The answer is Xplenty. Our data integration on the cloud lets you join data from various sources and then process it to gain new insights. What about storing the results back to RDS? No problemo, Xplenty does that as well.

Offload Redshift ETL to Xplenty

Offload Redshift ETL to Xplenty

Xplenty can read data from SQL Server, MongoDB, SAP HANA, and many more data stores. One of the many DBs Xplenty integrates with is Redshift.

Prepare Data for Analysis in Heroku

Prepare Data for Analysis in Heroku

Some developers need to process data. Maybe you work in a small startup where people take on several roles, or maybe in an enterprise company where you are asked to deal with Big Data. Either way, if you use Heroku, the most popular platform-as-a-service, there is a new add-on that can help you or the BI guy next door: Xplenty’s data integration on the cloud.

Processing JSON Data on the Cloud

Processing JSON Data on the Cloud

We’ve processed plenty of JSON data in our blog - from Tweets to GitHub commits - but we’ve never really discussed how to process JSON with Xplenty’s data integration on the cloud. Let's do so right now.

Parsing AWS CloudTrail Log Files

Parsing AWS CloudTrail Log Files

Amazon’s CloudTrail is a service that logs AWS activity. However, these logs need some preparation before they can be analyzed. In this post, we’ll see how to parse these log files with Xplenty’s data integration in the cloud to generate a comfortable tab-delimited file.

Parsing User Agent Strings in Big Data

Parsing User Agent Strings in Big Data

Big Data brother is watching - whenever users surf your website, their browser sends an HTTP header called ‘User Agent’. It tells your web server which browser they’re using, in which version, and on which operating system. The user agent string is logged by the web server and can be later analyzed to find out, for example, how many users still surf your website in old IE versions and whether you should support them or not.

How to Parse Query String Parameters from URLs in Big Data

How to Parse Query String Parameters from URLs in Big Data

Parsing URL query string parameters is easy with Xplenty. You can take a huge pile of web server logs and analyze them via Xplenty’s visual interface. Let me show you how.

Data Integration on the Cloud with Heroku PostgreSQL and Xplenty

Data Integration on the Cloud with Heroku PostgreSQL and Xplenty

What can you do with data collected on Heroku PostgreSQL? How will you analyze it and integrate it? With Xplenty, of course! Xplenty lets you connect to a PostgreSQL database on Heroku, design a dataflow via an intuitive user interface, aggregate the data, and even save it back to PostgreSQL on Heroku or other databases and cloud storage services.

Cloud Data Integration with MongoHQ and Xplenty

Cloud Data Integration with MongoHQ and Xplenty

Xplenty loves MongoDB. We’ve already guided you how to integrate with MongoLab, and now we’ll show you how to do data integration on the cloud with Xplenty and MongoHQ.

Improving Pig Data Integration Performance with Join

Improving Pig Data Integration Performance with Join

Did you know that choosing the right join type for your data could improve your data integration performance? Given certain data sets, Pig provides several join algorithms that process them in the most optimal way, thus saving you (X)plenty of time. This post will review them.

Analyze Papertrail Logs on the Cloud with Xplenty

Analyze Papertrail Logs on the Cloud with Xplenty

Papertrail is a cloud-hosted log management service. We use it internally for business insights and eat our own dogfood by processing the logs on Xplenty, our data integration platform on the cloud. Let's see how easily you could also integrate logs collected by Papertrail with Xplenty for analysis and aggregation on the cloud.

Data Integration on the Cloud with MongoLab and Xplenty

Data Integration on the Cloud with MongoLab and Xplenty

Xplenty can integrate various data sources on the cloud. One of the services it can integrate with is MongoLab, a MongoDB-as-a-service. This post will demonstrate how to connect them together.

8 SQL-on-Hadoop Challenges

8 SQL-on-Hadoop Challenges

Introducing Apache Hadoop to the organization can be difficult - everyone is trained and experienced in the old ways of SQL and all the analytics tools integrate with SQL. Certain technologies can help make the transition to Hadoop easier by providing support for SQL on Hadoop.

Integrating Relational Databases with Apache Hadoop

Integrating Relational Databases with Apache Hadoop

Relational databases are here to stay - they are common, integrate with other information systems, do a good job of querying structured data, and people know how to use them. Some believe that adding Apache Hadoop to the organization means death to the good old RDBMS, but that is far from true.

MongoDB + Xplenty = Perfect Big Data Stack

MongoDB + Xplenty = Perfect Big Data Stack

We love things that are great in pairs, things like chocolate and vanilla, gin and tonic, and Simon and Garfunkel. This is why we are proud to announce the new integration between MongoDB and Xplenty.

Analyze AWS CloudFront Logs in 15 Minutes

Analyze AWS CloudFront Logs in 15 Minutes

In the first two posts in the series we learned how to scale data collection on the cloud and collect Big Data with S3/Cloudfront logging. Some of our customers started collecting Cloudfront logs and wondered what to do with them, so we decided to write one more post to show how to analyze AWS CloudFront logs with Xplenty’s Hadoop-as-a-Service. You can try this demo for free even if you don’t have an Xplenty account or any logs at the moment.

5 Funnel Analysis Technical Challenges

5 Funnel Analysis Technical Challenges

Funnel analysis is awesome. Whether your company has a checkout, a registration, or any kind of process on a website or even in real life, funnel analysis lets you see how many customers are lost at each step of the way before reaching the golden goal of conversion. It helps you find troublesome steps on the path, fix them, and improve conversion rates.

Collecting Big Data with S3/CloudFront Logging

Collecting Big Data with S3/CloudFront Logging

In our recent article, Scale Your Data Collection on the Cloud Like a Champ, we reviewed several ways of collecting big data, the most promising of which was S3/CloudFront logging. It’s low cost and quick to implement. In this article we’d like to go deeper into the woods and show how to setup S3/CloudFront logging with your application.

Scale Your Data Collection on the Cloud Like a Champ

Scale Your Data Collection on the Cloud Like a Champ

When we met with WalkMe, a company which offers helpful in-app walkthroughs (we use it for our app and it’s great), our meeting took a surprising turn. We expected a discussion about crunching big data. They already had a data collection mechanism in place, but they had a problem that preceded any sort of crunching. They had a problem scaling the data collection process.