"data science" entries

Coming full circle with Bigtable and HBase

The O'Reilly Data Show Podcast: Michael Stack on HBase past, present, and future.

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At least once a year, I sit down with Michael Stack, engineer at Cloudera, to get an update on Apache HBase and the annual user conference, HBasecon. Stack has a great perspective, as he has been part of HBase since its inception. As former project leader, he remains a key contributor and evangelist, and one of the organizers of HBasecon.

In the beginning: Search and Bigtable

During the latest episode of the O’Reilly Data Show Podcast, I decided to broaden our conversation to include the beginnings of the very popular Apache HBase project. Stack reminded me that in the early days much of the big data community in the SF Bay Area was centered around search technologies, such as HBase. In particular, HBase was inspired by work out of Google (Bigtable), and the early engineers had ties to projects out of the Internet Archive:

At the time, I was working at the Internet Archive, and I was working on crawlers and search. The Bigtable paper looked really interesting to us because the archive, as you know, we used to host — or still do — the Wayback Machine. The Wayback Machine is a picture of the Web that goes back to 1998, and you could look at the Web at any particular time. What pages looked liked at a particular time. Bigtable was very interesting at the Internet Archive because it had this time dimension.

A group had started up to talk about the possibility of implementing a Bigtable clone. It was centered at a place called Powerset, a startup that was in San Francisco back then. That was about doing a search, so I went and talked to them. They said, ‘Come on over and we’ll make a space for doing a Bigtable clone.’ They had a very intricate search pipeline, and it was based on early Amazon AWS, and every time they started up their pipeline, they’d get a phone call from Amazon saying, ‘Please stop whatever it is you’re doing.’ … The first engineer would be a fellow called Jim Kellerman. The actual first 30 classes came from Mike Cafarella. He was instrumental in getting the first versions of Hadoop going. He was hanging around Apache Nutch at the time. … Doug [Cutting] used to work at the Internet archive, and the first actual versions of Hadoop were run on racks at the Internet archive. Doug was working on fulltext search. Then he moved on to go to Yahoo, to work on Hadoop full time.

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Squaring big data with database queries

Integrating open source tools into a data warehouse has its advantages.

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Although next-gen big data tools such as Hadoop, Spark, and MongoDB are finding more and more uses, most organizations need to maintain data in traditional relational stores as well. Deriving the benefits of both key/value stores and relational databases takes a lot of juggling. Three basic strategies are currently in use.

  • Double up on your data storage. Log everything in your fast key/value repository and duplicate part of it (or perform some reductions and store the results) in your relational data warehouse.
  • Store data primarily in a relational data warehouse, and use extract, transform, and load (ETL) tools to make it available for analytics. These tools run a fine-toothed comb through data to perform string manipulation, remove outlier values, etc. and produce a data set in the format required by data processing tools.
  • Put each type of data into the repository best suited to it––relational, Hadoop, etc.––but run queries between the repositories and return results from one repository to another for post-processing.

The appeal of the first is a large-scale simplicity, in that it uses well-understood systems in parallel. The second brings the familiarity of relational databases for business users to access. This article focuses on the third solution, which has advantages over the others: it avoids the redundancy of the first solution and is much easier to design and maintain than the second. I’ll describe how it is accomplished by Teradata, through its appliances and cloud solutions, but the building blocks are standard, open source tools such as Hive and HCatalog, so this strategy can be implemented by anyone. Read more…

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The log: The lifeblood of your data pipeline

Why every data pipeline should have a Unified Logging Layer.

The value of log data for business is unimpeachable. On every level of the organization, the question, “How are we doing?” is answered, ultimately, by log data. Error logs tell developers what went wrong in their applications. User event logs give product managers insights on usage. If the CEO has a question about the next quarter’s revenue forecast, the answer ultimately comes from payment/CRM logs. In this post, I explore the ideal frameworks for collecting and parsing logs.

Apache Kafka Architect Jay Kreps wrote a wonderfully crisp survey on log data. He begins with the simple question of “What is the log?” and elucidates its key role in thinking about data pipelines. Jay’s piece focuses mostly on storing and processing log data. Here, I focus on the steps before storing and processing.

Changing the way we think about log data

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The old paradigm — machines to humans, and the new — machines to machines. Image courtesy of Kiyoto Tamura.

Over the last decade, the primary consumer of log data shifted from humans to machines.

Software engineers still read logs, especially when their software behaves in an unexpected manner. However, in terms of “bytes processed,” humans account for a tiny fraction of the total consumption. Much of today’s “big data” is some form of log data, and businesses run tens of thousands of servers to parse and mine these logs to gain competitive edge. Read more…

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Investigating Spark’s performance

A deep dive into performance bottlenecks with Spark PMC member Kay Ousterhout.

Ousterhout_WebcastFor many who use and deploy Apache Spark, knowing how to find critical bottlenecks is extremely important. In a recent O’Reilly webcast, Making Sense of Spark Performance, Spark committer and PMC member Kay Ousterhout gave a brief overview of how Spark works, and dove into how she measured performance bottlenecks using new metrics, including block-time analysis. Ousterhout walked through high-level takeaways from her in-depth analysis of several workloads, and offered a live demo of a new performance analysis tool and explained how you can use it to improve your Spark performance.

Her research uncovered surprising insights into Spark’s performance on two benchmarks (TPC-DS and the Big Data Benchmark), and one production workload. As part of our overall series of webcasts on big data, data science, and engineering, this webcast debunked commonly held ideas surrounding network performance, showing that CPU — not I/O — is often a critical bottleneck, and demonstrated how to identify and fix stragglers.

Network performance is almost irrelevant

While there’s been a lot of research work on performance — mainly surrounding the issues of whether to cache input data in-memory or on machine, scheduling, straggler tasks, and network performance — there haven’t been comprehensive studies into what’s most important to performance overall. This is where Ousterhout’s research comes in — taking on what she refers to as “community dogma,” beginning with the idea that network and disk I/O are major bottlenecks. Read more…

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Zeta Architecture: Hexagon is the new circle

An enterprise architecture solution for scale and efficiency.

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Data processing in the enterprise goes very swiftly from “good enough” to “we need to be faster!” as expectations grow. The Zeta Architecture is an enterprise architecture that enables simplified business processes and defines a scalable way for increasing the speed of integrating data into the business. Following a bit of history and a description of the architecture, I’ll use Google as an example and look at the way the company deploys technologies for Gmail.

Origin story and motivation

I’ve worked on a variety of different information systems over my career, each with their own classes of challenge. The most interesting from a capacity perspective was for a company that delivers digital advertising. The biggest technical problems in that industry flow from the sheer volume of transactions that occur on a daily basis. Traffic flows in all hours of the day, but there are certainly peak periods, which means all planning must revolve around the capacity during the peak hours. This solution space isn’t altogether different than that of Amazon; they had to build their infrastructure to handle massive loads of peak traffic. Both Amazon and digital advertising, incidentally, have a Black Friday spike.

Many different architectural ideas came to my mind while I was in digital advertising. Real-time performance tracking of the advertising platform was one such thing. This was well before real-time became a hot buzzword in the technology industry. There was a point in time where this digital advertising company was “satisfied” with, or perhaps tolerated, having a two-to-three-hour delay between making changes to the system and having complete insight into the effects of the changes. After nearly a year at this company, I was finally able to get a large architectural change made to streamline log collection and management. Before the implementation started, I told everyone involved what would happen. Although this approach would enable the business to see the performance within approximately 5-10 minutes of the time a change was made, that this would not be good enough after people got a feel for what real-time could deliver. Since people didn’t have that taste in their mouths, they wouldn’t yet support going straight to real-time for this information. The implementation of this architecture was in place a few months after I departed the company for a new opportunity. The implementation worked great, and after about three months of experience with the new architecture, my former colleagues contacted me and told me they were looking to re-architect the entire solution to go to real time. Read more…

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The VR growth cycle: What’s different this time around

A chat with Tony Parisi on where we are with VR, where we need to go, and why we're going to get there this time.

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Consumer virtual reality (VR) is in the midst of a dizzying and exhilarating upswing. A new breed of systems, pioneered by Oculus and centered on head-worn displays with breakthrough quality, are minting believers — whether investors, developers, journalists, or early-adopting consumers. Major new hardware announcements and releases are occurring on a regular basis, game studios and production houses big and small are tossing their hats into the ring, and ambitious startups are getting funded to stake out many different application domains. Is it a boom, a bubble, or the birth of a new computing platform?

Underneath this fundamental quandary, there are many basic questions that remain unresolved: Which hardware and software platforms will dominate? What input and touch feedback technologies will prove themselves? What are the design and artistic principles in this medium? What role will standards play, who will develop them, and when? The list goes on.

For many of these questions, we’ll need to wait a bit longer for answers to emerge; like smartphones in 2007, we can only speculate about, say, the user interface conventions that will emerge as designers grapple with this new paradigm. But on other issues, there is some wisdom to be gleaned. After all, VR has been around for a long time, and there are some poor souls who have been working in the mines all along. Read more…

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Building big data systems in academia and industry

The O'Reilly Data Show Podcast: Mikio Braun on stream processing, academic research, and training.

Mikio Braun is a machine learning researcher who also enjoys software engineering. We first met when he co-founded a real-time analytics company called streamdrill. Since then, I’ve always had great conversations with him on many topics in the data space. He gave one of the best-attended sessions at Strata + Hadoop World in Barcelona last year on some of his work at streamdrill.

I recently sat down with Braun for the latest episode of the O’Reilly Data Show Podcast, and we talked about machine learning, stream processing and analytics, his recent foray into data science training, and academia versus industry (his interests are a bit on the “applied” side, but he enjoys both).

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An example of a big data solution. Source: Mikio Braun, used with permission.

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A real-time tool for a real-time problem

Using VoltDB and the Lambda Architecture to locate abnormal behavior.

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Subscriber Identity Module box (SIMbox) fraud is a type of telecommunications fraud where users avoid an international outbound-calls charge by redirecting the call through voice over IP to a SIM in the country where the destination is located. This is an issue we helped a client address at Wise Athena.

Taking on this type of problem requires a stream-based analysis of the Call Detail Record (CDR) logs, which are typically generated quickly. Detecting this kind of activity requires in-memory computations of streaming data. You might also need to scale horizontally.

We recently evaluated the use of VoltDB together with our cognitive analytics and machine-learning system to analyze CDRs and provide accurate and fast SIMbox fraud detection. At the beginning, we used batch processing to detect SIMbox fraud, but the response time took too long, so we switched to a technology that allows in-memory computations in order to reach the desired time constraints.

VoltDB’s in-memory distributed database provides transactions at streaming speed in a fast environment. It can support millions of small transactions per second. It also allows streaming aggregation and fast counters over incoming data. These attributes allowed us to develop a real-time analytics layer on top of VoltDB. Read more…

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A real-time processing revival

Things are moving fast in the stream processing world.

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Register for Strata + Hadoop World, London. Editor’s note: Ben Lorica is an advisor to Databricks and Graphistry. Many of the technologies discussed in this post will be covered in trainings, tutorials, and sessions at Strata + Hadoop World in London this coming May.

There’s renewed interest in stream processing and analytics. I write this based on some data points (attendance in webcasts and conference sessions; a recent meetup), and many conversations with technologists, startup founders, and investors. Certainly, applications are driving this recent resurgence. I’ve written previously about systems that come from IT operations as well as how the rise of cheap sensors are producing stream mining solutions from wearables (mostly health-related apps) and the IoT (consumer, industrial, and municipal settings). In this post, I’ll provide a short update on some of the systems that are being built to handle large amounts of event data.

Apache projects (Kafka, Storm, Spark Streaming, Flume) continue to be popular components in stream processing stacks (I’m not yet hearing much about Samza). Over the past year, many more engineers started deploying Kafka alongside one of the two leading distributed stream processing frameworks (Storm or Spark Streaming). Among the major Hadoop vendors, Hortonworks has been promoting Storm, Cloudera supports Spark Streaming, and MapR supports both. Kafka is a high-throughput distributed pub/sub system that provides a layer of indirection between “producers” that write to it and “consumers” that take data out of it. A new startup (Confluent) founded by the creators of Kafka should further accelerate the development of this already very popular system. Apache Flume is used to collect, aggregate, and move large amounts of streaming data, and is frequently used with Kafka (Flafka or Flume + Kafka). Spark Streaming continues to be one of the more popular components within the Spark ecosystem, and its creators have been adding features at a rapid pace (most recently Kafka integration, a Python API, and zero data loss). Read more…

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Entities move data from tables to knowledge

Cleaning and combining fields can turn messy data into actionable insight.

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We often talk in business and computing about moving from “raw data” to “knowledge,” hoping to take useful actions based on the data our organization has collected over time. Before one can view trends in your data or do other analytics, you need tools for data cleaning and for combining multiple data sources into meaningful collections of information, known as entities. An entity may be a customer, a product, a point of sale, an incident being investigated by the police, or anything else around which you want to build meaningful context.

In this post, we’ll explore some of the complexities in real-life data that create headaches — and how analytical software can help users prepare data for sophisticated queries and visualizations. Read more…

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