Most people think of orbitz.com, but Orbitz Worldwide is really a global portfolio of leading online travel consumer brands including Orbitz, Cheaptickets, The Away Network, ebookers and HotelClub. Orbitz also provides business to business services - Orbitz Worldwide Distribution provides hotel booking capabilities to a number of leading carriers such as Amtrak, Delta, LAN, KLM, Air France and Orbitz for Business provides corporate travel services to a number of Fortune 100 clients Orbitz started in 1999, orbitz site launched in 2001.
A couple of years ago when I mentioned Hadoop I’d often get blank stares, even from developers. I think most folks now are at least aware of what Hadoop is.
This chart isn’t exactly an apples-to-apples comparison, but provides some idea of the difference in cost per TB for the DW vs. Hadoop Hadoop doesn’t provide the same functionality as a data warehouse, but it does allow us to store and process data that wasn’t practical before for economic and technical reasons. Putting data into a DB or DWH requires having knowledge or making assumptions about how the data will be used. Either way you’re putting constraints around how the data is accessed and processed. With Hadoop each application can process the raw data in whatever way is required. If you decide you need to analyze different attributes you just run a new query.
The initial motivation was to solve a particular business problem. Orbitz wanted to be able to use intelligent algorithms to optimize various site functions, for example optimizing hotel search by showing consumers hotels that more closely match their preferences, leading to more bookings.
Improving hotel search requires access to such data as which hotels users saw in search results, which hotels they clicked on, and which hotels were actually booked. Much of this data was available in web analytics logs.
Management was supportive of anything that facilitated ML team efforts. But when we presented a hardware spec for servers with local non-raided storage, etc. syseng offered us blades with attached storage.
Hadoop is used to crunch data for input to a system to recommend products to users. Although we use third-party sites to monitor site performance, Hadoop allows the front end team to provide detailed reports on page download performance, providing valuable trending data not available from other sources. Data is used for analysis of user segments, which can drive personalization. This chart shows that Safari users click on hotels with higher mean and median prices as opposed to other users. This is just a handful of examples of how Hadoop is driving business value.
Recently received an email from a user seeking access to Hive. Sent him a detailed email with info on accessing Hive, etc. Received an email back basically saying “you lost me at ssh”.
Previous to 2011 Hadoop responsibilities were split across technology teams. Moving under a single team centralized responsibility and resources for Hadoop.
Processing of click data gathered by web servers. This click data contains marketing info. data cleansing step is done inside data warehouse using a stored procedure further downstream processing is done to generate final data sets for reporting Although this processing generates the required user reports, this process consumes considerable time and resources on the data warehouse, consuming resources that could be used for reports, queries, etc.
ETL step is eliminated, instead raw logs will be uploaded to HDFS which is a much faster process Moving the data cleansing to MapReduce will allow us to take advantage of Hadoop’s efficiencies and greatly speed up the processing. Moves the “heavy lifting” of processing the relatively large data sets to Hadoop, and takes advantage of Hadoop’s efficiencies.
Bad news is we need to significantly increase the number of servers in our cluster, the good news is that this is because teams are using Hadoop, and new projects are coming online.
Architecting for Big Data Integrating Hadoop into an Enterprise Data Infrastructure Raghu Kashyap and Jonathan Seidman Gartner Peer Forum September 14 | 2011
We faced organizational resistance to deploying Hadoop.
Not from management, but from other technical teams.
Required persistence to convince them that we needed to introduce a new hardware spec to support Hadoop.
Current Big Data Infrastructure Hadoop page MapReduce HDFS MapReduce Jobs (Java, Python, R/RHIPE) Analytic Tools (Hive, Pig) Data Warehouse (Greenplum) psql, gpload, Sqoop External Analytical Jobs (Java, R, etc.) Aggregated Data Aggregated Data