engineering.gnip.com

Engineering Blog – Big Data Engineering and Development Blog – Gnip

Plugged In to Gnip. Enriching With Redis Part II: Schema Happens. July 15, 2014. As a follow up to our first post about Redis at Gnip. We’ll discuss how we built on our basic client side Redis usage. Early on our Redis usage implementation was great. It was performant, we had failover, replication, and health checks. However a lot of our client code for. How do I serialize/deserialize this value? What happens if it fails, or I need to retry? Does this operation need to be pipelined? GeoEntityBucketDefini...

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Engineering Blog – Big Data Engineering and Development Blog – Gnip | engineering.gnip.com Reviews

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Plugged In to Gnip. Enriching With Redis Part II: Schema Happens. July 15, 2014. As a follow up to our first post about Redis at Gnip. We’ll discuss how we built on our basic client side Redis usage. Early on our Redis usage implementation was great. It was performant, we had failover, replication, and health checks. However a lot of our client code for. How do I serialize/deserialize this value? What happens if it fails, or I need to retry? Does this operation need to be pipelined? GeoEntityBucketDefini...

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Engineering Blog – Big Data Engineering and Development Blog – Gnip | engineering.gnip.com Reviews

https://engineering.gnip.com

Plugged In to Gnip. Enriching With Redis Part II: Schema Happens. July 15, 2014. As a follow up to our first post about Redis at Gnip. We’ll discuss how we built on our basic client side Redis usage. Early on our Redis usage implementation was great. It was performant, we had failover, replication, and health checks. However a lot of our client code for. How do I serialize/deserialize this value? What happens if it fails, or I need to retry? Does this operation need to be pipelined? GeoEntityBucketDefini...

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engineering.gnip.com

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Engineering Blog – Big Data Engineering and Development Blog – Gnip

https://engineering.gnip.com/enriching-with-redis-part-1

Enriching With Redis: Part 1. May 2, 2014. We’ve talked previously about how we use Redis to Track Cluster Health. For our Historical PowerTrack. Now we’ll be exploring how we enrich with Redis. For every activity that flows through Gnip, we add extra metadata that provides additional information that our customers are interested in. We call this extra metadata Enrichments, and it’s what allows customers to filter on URLs or Profile Location. But for a quick overview, this enrichment takes the opaque, us...

2

Engineering Blog – Big Data Engineering and Development Blog – Gnip

https://engineering.gnip.com/category/operations

Exploring S3 Read Performance. November 13, 2013. Jud Valeski, Co-Founder & CTO. This blog post is a collaboration between Jud Valeski and Nick Matheson. When you use Peta as the unit to measure your S3 storage usage, you’re dealing with a lot of data. Gnip has been a tremendously heavy S3 user since S3 came online; it remains our large-scale durable storage solution. One such approach is using HTTP byte range requests into our S3 buckets in order to access just the blocks of data we’re interested in&#46...

3

Engineering Blog – Big Data Engineering and Development Blog – Gnip

https://engineering.gnip.com/tag/memory-optimization

Tag Archives: memory optimization. Enriching With Redis: Part 1. May 2, 2014. We’ve talked previously about how we use Redis to Track Cluster Health. For our Historical PowerTrack. Now we’ll be exploring how we enrich with Redis. For every activity that flows through Gnip, we add extra metadata that provides additional information that our customers are interested in. We call this extra metadata Enrichments, and it’s what allows customers to filter on URLs or Profile Location. But for a quick overview, t...

4

Engineering Blog – Big Data Engineering and Development Blog – Gnip

https://engineering.gnip.com/author/davidvollmar

Bend, Don’t Break. February 4, 2014. David Vollmar, Software Engineer. This post explains how we solved a major problem we faced: the challenge of scaling our realtime data processing systems to handle ever-increasing volumes without drastically over-building. By using Kafka as a queuing system in the initial ingest, we’ve been able to build a system that elegantly scales to handle unpredictable load and that lets us go wide when we need to process higher than normal data volumes. Bend, Don’t Break.

5

Engineering Blog – Big Data Engineering and Development Blog – Gnip

https://engineering.gnip.com/tag/schema

Enriching With Redis Part II: Schema Happens. July 15, 2014. As a follow up to our first post about Redis at Gnip. We’ll discuss how we built on our basic client side Redis usage. Early on our Redis usage implementation was great. It was performant, we had failover, replication, and health checks. However a lot of our client code for. Redis had become pretty low level. For instance code to read or write an enrichment had to worry about, and implement things like: Which key is this going to live in? GeoEn...

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blog.gnip.com

Zach HoferShall, Head of Twitter Ecosystem - Gnip Blog - Social Data and Data Science Blog

https://blog.gnip.com/author/zachhofershall

Author: Zach Hofer-Shall, Head of Twitter Ecosystem. Zach Hofer-Shall is the Head of Twitter Ecosystem where he manages Twitter's B2B data and platform partners and manages the Twitter Certified Program team. Prior to Twitter, Zach was a Senior Analyst with Forrester Research covering social technology platforms, social strategy, and social intelligence. He resides in Oakland with his wife and twin boys. Working Directly With the Twitter Data Ecosystem. April 10, 2015. If you’re one of the companies stil...

blog.gnip.com

Uncategorized Archives - Gnip Blog - Social Data and Data Science Blog

https://blog.gnip.com/category/uncategorized

The Gnip Usage API: A New Tool for Monitoring Data Consumption. July 9, 2014. Adam Tornes, Product Manager. At Gnip we know that reliable, sustainable, and complete data delivery products are core to enabling our customers to surface insights and value from social data. Today we’re excited to announce a new API that will make it easier for our customers to monitor and manage the volume of Gnip data they consume – the Usage API. The Usage API is available for use today. To learn more about the Usage A...

blog.gnip.com

Gnip Products Archives - Gnip Blog - Social Data and Data Science Blog

https://blog.gnip.com/category/gnip-products

Category Archives: Gnip Products. Historical PowerTrack Requests, Now Faster Than Ever. September 4, 2014. Adam Tornes, Product Manager. The Twitter Data Product Team is excited to share an update with you around recent enhancements to our Historical PowerTrack offering. In an effort to improve our customer experience for historical data requests, we’ve made substantial technology investments to reduce processing times as well as to support future adoption and usage patterns. One final area of improvemen...

blog.gnip.com

Amanda Bridenhagen, Plugged In Marketing Manager - Gnip Blog - Social Data and Data Science Blog

https://blog.gnip.com/author/amandabridenhagen

The Power of Command Centers. June 24, 2014. Amanda Bridenhagen, Plugged In Marketing Manager. A command center is just one of the tools that Gnip customer and new Plugged In partner MutualMind. We love highlighting the ways our customers make life easier for their clients and we’re excited to add MutualMind to the partner program! Plugged In to Gnip. Smoke vs. Smoke: DiscoverText helps public health researchers. June 12, 2014. Amanda Bridenhagen, Plugged In Marketing Manager. These days manually sorting...

blog.gnip.com

Twitter and SXSW: Barometer of Trends

https://blog.gnip.com/twitter-and-sxsw

Twitter and SXSW: Barometer of Trends. November 2, 2012. Scott Hendrickson, Chief Data Scientist. Last week we talked about tracking SXSW from 2007 to 2012. Using Gnip’s Historical PowerTrack for Twitter. This gave us insight into year-over-year trends in SXSW Tweets and now we’re going to look at how SXSW trends have changed over time. Party vs. Panel. BBQ vs. Tacos. This next trend might seem silly, who cares if more people are interested in BBQ or Tacos? While many have long suspected that Austin was ...

blog.gnip.com

Jim Moffit, Developer Advocate - Gnip Blog - Social Data and Data Science Blog

https://blog.gnip.com/author/jmoffit

Author: Jim Moffit, Developer Advocate. Jim Moffitt is a developer advocate at Twitter, where he helps developers and customers build amazing things with Twitter data. Before Twitter, Jim developed software for real-time weather monitoring and early-warning systems. In his free time, Jim loves hanging out with his family, enjoying the Colorado outdoors, travel, photography, gardening, and other random hobbies. Tweeting in the Rain, Part 4: Tweets during the 2013 Colorado flood. September 12, 2014. As the...

blog.gnip.com

Twitter and IBM Partner to Transform Decision Making - Gnip Blog - Social Data and Data Science Blog

https://blog.gnip.com/twitter-and-ibm-partner

Twitter and IBM Partner to Transform Decision Making. October 29, 2014. Chris Moody, VP Data Strategy at Twitter. I’m thrilled to announce that Twitter and IBM are partnering to transform how businesses and institutions understand their customers, markets and trends and inform every business decision. For details, see our post on the Twitter blog. And IBM’s press release. More from the blog. Newest Gnip Data Publisher: WordPress. Get your Powered by Gnip Badge. Adobe Adds Foursquare to Adobe Social.

blog.gnip.com

Tweeting in the Rain, Part 3 - A Review of Early Warning Systems

https://blog.gnip.com/tweeting-in-the-rain-part-3

Tweeting in the Rain, Part 3. September 11, 2013. Jim Moffit, Developer Advocate. This is part 3 of our series looking at how social data can create a signal about major rain events. Part 1. Examines whether local rain events produce a Twitter signal. Part 2. Looks at the technology needed to detect a Twitter signal.). What opportunities do social networks bring to early-warning systems? For an introduction to this project). What are some example broadcast use-cases? Observations and predictions from reg...

blog.gnip.com

Historical PowerTrack Requests, Now Faster Than Ever

https://blog.gnip.com/historical-powertrack-requests-now-faster-ever

Historical PowerTrack Requests, Now Faster Than Ever. September 4, 2014. Adam Tornes, Product Manager. The Twitter Data Product Team is excited to share an update with you around recent enhancements to our Historical PowerTrack offering. In an effort to improve our customer experience for historical data requests, we’ve made substantial technology investments to reduce processing times as well as to support future adoption and usage patterns. One final area of improvement benefiting your business is our ...

blog.gnip.com

Tweeting in the Rain, Part 4: Tweets during the 2013 Colorado flood - Gnip Blog - Social Data and Data Science Blog

https://blog.gnip.com/tweeting-rain-part-4-tweets-2013-colorado-flood

Tweeting in the Rain, Part 4: Tweets during the 2013 Colorado flood. September 12, 2014. Jim Moffit, Developer Advocate. In August 2013, we posted two Tweeting in the Rain ( Part 1. Around noon on Wednesday, September 11, 2013, we posted Part 3. On the evening of September 11, we stayed up late watching the flood and its aftermath unfold on Twitter, 140 characters at a time. As written about here. 1 The ability for the public to share photos and videos in real-time. With these questions in mind, we began...

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Plugged In to Gnip. Enriching With Redis Part II: Schema Happens. July 15, 2014. As a follow up to our first post about Redis at Gnip. We’ll discuss how we built on our basic client side Redis usage. Early on our Redis usage implementation was great. It was performant, we had failover, replication, and health checks. However a lot of our client code for. How do I serialize/deserialize this value? What happens if it fails, or I need to retry? Does this operation need to be pipelined? GeoEntityBucketDefini...

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