[A version of this post appears on the O’Reilly Strata blog.] Largely unknown outside data engineering circles, Apache Kafka is one of the more popular open source, distributed computing projects. Many data engineers I speak with either already use it or are planning to do so. It is a distributed message broker used to store1Continue reading “Stream Processing and Mining just got more interesting”
Author Archives: Ben Lorica
How Twitter monitors millions of time-series
[A version of this post appears on the O’Reilly Strata blog.] One of the keys to Twitter’s ability to process 500 millions tweets daily is a software development process that values monitoring and measurement. A recent post from the company’s Observability team detailed the software stack for monitoring the performance characteristics of software services, andContinue reading “How Twitter monitors millions of time-series”
Data Analysis: Just one component of the Data Science workflow
[A version of this post appears on the O’Reilly Strata blog.] Judging from articles in the popular press the term data scientist has increasingly come to refer to someone who specializes in data analysis (statistics, machine-learning, etc.). This is unfortunate since the term originally described someone who could cut across disciplines. Far from being confinedContinue reading “Data Analysis: Just one component of the Data Science workflow”
Running batch and long-running, highly available service jobs on the same cluster
[A version of this post appears on the O’Reilly Strata blog.] As organizations increasingly rely on large computing clusters, tools for leveraging and efficiently managing compute resources become critical. Specifically, tools that allow multiple services and frameworks run on the same cluster can significantly increase utilization and efficiency. Schedulers1 take into account policies and workloadsContinue reading “Running batch and long-running, highly available service jobs on the same cluster”
Data analysis tools target non-experts
[A version of this post appears on the O’Reilly Strata blog.] A new set of tools make it easier to do a variety of data analysis tasks. Some require no programming, while other tools make it easier to combine code, visuals, and text in the same workflow. They enable users who aren’t statisticians or dataContinue reading “Data analysis tools target non-experts”
Interactive Big Data analysis using approximate answers
[A version of this post appears on the O’Reilly Strata blog.] Interactive query analysis for (Hadoop scale data) has recently attracted the attention of many companies and open source developers – some examples include Cloudera’s Impala, Shark, Pivotal’s HAWQ, Hadapt, CitusDB, Phoenix, Sqrrl, Redshift, and BigQuery. These solutions use distributed computing, and a combination ofContinue reading “Interactive Big Data analysis using approximate answers”
Surfacing anomalies and patterns in Machine Data
[A version of this post appears on the O’Reilly Strata blog.] I’ve been noticing that many interesting big data systems are coming out of IT operations. These are systems that go beyond the standard “capture/measure, display charts, and send alerts”. IT operations has long been a source of many interesting big data1 problems and IContinue reading “Surfacing anomalies and patterns in Machine Data”
Big Data and Advertising: In the trenches
[A version of this post appears on the O’Reilly Strata blog.] The $35B merger of Omnicom and Publicis put the convergence of Big Data and Advertising1 in the front pages of business publications. Adtech2 companies have long been at the forefront of many data technologies, strategies, and techniques. By now it’s well-known that many impressiveContinue reading “Big Data and Advertising: In the trenches”
Near realtime, streaming, and perpetual analytics
[A version of this post appears on the O’Reilly Strata blog.] Simple example of a near realtime app built with Hadoop and HBase Over the past year Hadoop emerged from its batch processing roots and began to take on interactive and near realtime applications. There are numerous examples that fall under these categories, but oneContinue reading “Near realtime, streaming, and perpetual analytics”
Tightly integrated engines streamline Big Data analysis
[A version of this post appears on the O’Reilly Strata blog.] The choice of tools for data science includes1 factors like scalability, performance, and convenience. A while back I noted that data scientists tended to fall into two camps: those who used an integrated stack, and others who tended to stitch together frameworks. Being ableContinue reading “Tightly integrated engines streamline Big Data analysis”