Posts tagged Tool

Facebook Introduces New Tool To Measure Sales Driven By Ads by @mattsouthern

It has always been easy to measure how well Facebook ads are performing when it comes to clicks and views, but what about sales? Measuring the amount of new business you’re actually getting from Facebook ads, especially offline, is about to get a lot easier the company explains in an announcement today. With a capability called ‘conversion lift measurement’, Facebook says it can help advertisers more accurately measure how much new business is being driven by ads. Here’s how Facebook measures conversion lift: When a Facebook campaign begins, two groups are created. One is a random test group of people […]

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The Big-Data Tool Spark May Be Hotter Than Hadoop, But It Still Has Issues

Hadoop is hot. But its kissing cousin Spark is even hotter.

Indeed, Spark is hot like Apache Hadoop was half a decade ago. Spawned at UC Berkeley’s AMPLab, Spark is a fast data processing engine that works in the Hadoop ecosystem, replacing MapReduce. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and iterative algorithms, like those commonly found in machine learning and graph processing.

San Francisco-based Typesafe, sponsors of a popular survey on Java developers I wrote about last year and the commercial backers of Scala, Play Framework, and Akka, recently conducted a survey of developers about Spark. More than 2,000 (2,136 to be exact) developers responded. Of the findings, three conclusions jump out:

  1. Spark awareness and adoption are seeing hockey-stick-like growth. Google Trends confirms this. The survey shows that 71% of respondents have at least evaluation or research experience with Spark, and 35% are now using it or plan to use it.
  2. Faster data processing and event streaming are the focus for enterprises. By far the most desirable features are Spark’s vastly improved processing performance over MapReduce (over 78% mention this) and the ability to process event streams (over 66% mention this), which MapReduce cannot do.
  3. Perceived barriers to adoption are not major blockers. When asked what’s holding them back from the Spark revolution, respondents mentioned their own lack of experience with Spark and the need for more detailed documentation, especially for more advanced application scenarios and performance tuning. They mentioned perceived immaturity, in general, and also integration with other middleware, like message queues and databases. Lack of commercial support, which is still spotty even by the Hadoop vendors, was also a concern. Finally, some respondents mentioned that their organizations aren’t in need of big data solutions at this time.

I spoke to Typesafe’s architect for Big Data Products and Services, Dean Wampler (@deanwampler), on his thoughts about the rise of Spark. Wampler recently recorded a talk on why he thinks Spark/Scala are rapidly replacing MapReduce/Java as the most popular Big Data compute engine in the enterprise.

Striking The Spark

Dean Wampler

ReadWriteFor those venturing into Spark, what are the most common hurdles?

Wampler: It’s mostly around things like acquiring expertise, having good documentation with deep, non-trivial examples. Many people aren’t sure how to manage, monitor, and tune their jobs and clusters. Commercial support for Spark is still limited, especially for non-YARN deployments. However, even among the Hadoop vendors, support is still spotty. 

Spark still needs to mature in many ways, especially the newer modules, such as Spark SQL and Spark Streaming. Older tools, like Hadoop and MapReduce, have had a longer runway and hence more time to be hardened and expertise to be documented. All these issues are being addressed and they should be resolved relatively soon.

RWI hear people ask “where are you running Spark?” all the time, suggesting a pretty broad range of resource management strategies, e.g., standalone clusters, YARN, Mesos. Do you believe industry will tend to run Big Data clusters in isolation, or do you see the industry eventually moving to running Big Data clusters alongside other applications in production? 

DW: I think most organizations will still use fewer, larger clusters, just so their operations teams have fewer clusters to watch. Mesos and YARN really make this approach attractive. Conversely, Spark makes it easier to set up small, dedicated clusters for specific problems. Say you’re ingesting the Twitter firehose. You might want a dedicated cluster tuned optimally for that streaming challenge. Maybe it forwards “curated” data to another cluster, say a big one used for data warehousing.

Keeping The Spark Alive

RWIs the operations side of Spark different than the operations side of MapReduce?

DW: For batch jobs, it’s about the same. Streaming jobs, however, raise new challenges. 

For a typical batch job, whether it’s written in Spark or MapReduce, you submit a job to run, it gets its resources from YARN or Mesos, and once it finishes, the resources are released. However, in Spark streaming, the jobs run continuously, so you might need more robust recovery if the job dies, so stream data isn’t lost. 

Another problem is resource allocation. For a batch job, it’s probably okay to give it a set of resources and have those resources locked up for the job’s life cycle. (Note, however, some dynamic management is already done by YARN and Mesos.) Long-running jobs really need more dynamic resource management, so you don’t have idle resources during relatively quiescent periods, or overwhelmed resources during peak times. 

Hence, you really want the ability grow and shrink resource allocations, where scaling up and down is automated. This is not a trivial problem to solve and you can’t rely on human intervention either.

RW: Let’s talk about the Scala / Spark connection. Does Spark require knowledge of Scala? Are most people using Spark also well versed in Scala? And is it more the case that Scala users are those who tend to favor Spark, or is Spark creating a “pull” effect into Scala?

DW: Spark is written in Scala and it is pulling people towards Scala. Typically they’re coming from a Big Data ecosystem already, and they are used to working with Java, if they are developers, or languages like Python and R, if they are data scientists. 

Fortunately for everyone, Spark supports several languages – Scala, Java, Python, and R is coming. So people don’t necessarily have to switch to Scala. 

There has been a lag in the API coverage for the other languages, but the Spark team has almost closed the gap. The rule of thumb is that you’ll get the best runtime performance if you use Scala or Java, and you’ll get the most concise code if you use Scala or Python. So, Spark is actually drawing people to Scala, but it doesn’t require that you have to be a Scala expert. 

I like the fact that Spark uses the more mainstream features of Scala. It doesn’t require mastery of more advanced constructs.

Photo courtesy of Shutterstock

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SearchCap: Google Firefox Push, Bing Ads Preview Tool & YP Cross Retargeting

Below is what happened in search today, as reported on Search Engine Land and from other places across the web.

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Bing Ads New Bid Preview Tool Lets You See Effects Of Bid Changes In Real Time

See how ads will display with bid increases before committing to new keyword bids.

The post Bing Ads New Bid Preview Tool Lets You See Effects Of Bid Changes In Real Time appeared first on Search Engine Land.



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Google Updates Its Structured Data Testing Tool, Documentation & Syntax Support

Google has unleashed a new and improved Structured Data Testing tool, updated their documentation and guidelines, while adding more markup support.

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Google Updates Their Structured Data Testing Tool, Documentation & Syntax Support

Google has unleashed a new and improved Structured Data Testing tool, updated their documentation and guidelines, while adding more markup support.

The post Google Updates Their Structured Data Testing Tool, Documentation & Syntax Support appeared first on Search Engine Land.



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Google Releases New Update To Structured Data Site Testing Tool by @mattsouthern

Structured data can give you that edge you need when it comes to getting content discovered and clicked on in search results, and other Google properties. Google’s structured data testing tool has always been the best way to ensure your markup code has been implemented correctly. Several updates released for that tool today will make it even more useful. A complete list of updates are as follows: A new structured data testing tool, which is said to better reflect Google’s interpretation of your content. Improved documentation and policy guidelines for Google features powered by structured data on the web Expanded […]

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Search Is Number One Content Discovery Tool For Mobile Users

Yesterday the IAB released a report that seeks to “rehabilitate” the mobile web. Based on a Harris survey of 2,000 US mobile internet users, it argues that despite the gap in time spent, mobile users value and heavily utilize the mobile web. Interestingly survey respondents perceived…



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Facebook Introduces ‘Work’, A Social Tool For Businessess and Employees by @mattsouthern

Though we may try to hide it, it’s no secret that we browse Facebook at work. Now the social network wants to make it so we no longer have to hide it with a new product its working on to make social media more work-friendly. Simply titled ‘Work’, the new product will reportedly allow businesses to create self-contained social networks exclusively for employees. Sounds kind of like an Intranet, with the exception that it would be accessible outside of work. The social networks created in Work would mirror Facebook in design and user experience. Users can join using their existing Facebook […]

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LinkedIn Expands Its Blogging Tool To English Speaking Users Outside The US by @mattsouthern

LinkedIn’s publishing platform, previously only accessible to US users, has now been rolled out to more than 130 million English-speaking users outside of the United States. The company announced the expansion of its publishing tool yesterday, which now brings the total amount of people that could potentially publish content on LinkedIn to 230 million. With this change, LinkedIn is encouraging its users to treat the social network more like a blog — the company wants users to publish longer posts with photos and headlines versus just a simple status update, or a link to an article published elsewhere. LinkedIn’s publishing […]

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