The last few years has seen a massive change in the data landscape. With the rise of big data, there’s been rapid innovation in the tools, skills and roles working on data systems. Data architectures have evolved beyond monolithic, centralized databases and unwieldy analytic applications to distributed, scalable architectures with simpler collaborative and interactive analytic tools. In this post, I look at the defining features of modern data architectures.
Modern data architectures generally feature the following (though not all of these may be present in the same system):
Over the past few years, Kafka has become the most exciting new addition in the big data distributed architecture. Originally developed at LinkedIn, its founders Jay Kreps, Jun Rao and Neha Narkhede have launched a company Confluent to develop its open-core business model. The software at its core, Apache Kafka reinvents the database log to provide a highly scalable and fault tolerant, high performance distributed system, which serves as the data pipeline backbone for stream data processing.
Small files are a big problem in Hadoop.
Hadoop is designed to manage big data and by design this means HDFS is designed to store very large files in a distributed cluster with streaming access to this data. For reference, a typical block in HDFS is 64 MB or 128 MB. Each small file (few MB or less) is stored in a block and multiple small files could be stored in blocks across different nodes of the distributed cluster.
I recently gave a talk on data processing with Apache Spark using R and Python. tl;dr - the slides and presentation can be accessed below (free registration):
As noted in my previous post, Spark has become the defacto standard for big data applications and has been adopted quickly by the industry. See Cloudera’s One Platform initiative blog post by CEO Mike Olson for their commitment to Spark.
In data science R had seen rapid adoption, not only because it was open source and free compared to costly SAS, but also the huge number of statistical and graphical packages provided by R for data science.
I presented a talk last week introducing Data Science and associated topics to some enthusiasts.
Here’s a slide deck I created quickly with markdown using Swipe - a start-up building HTML5 presentation tools.
The contents include:
Data scientist skills Data science: enablers and barriers Big data analytics Data science lifecycle Use cases Tools and technology Project approach Machine learning Skills and roles Learning resources Here are the slides:
Sometime back I presented a webinar on BrightTalk. The slides for the talk have now been uploaded on Slideshare. The talk focused more on changes in digital technology disrupting businesses, the effect of Big Data, the FOMO (Fear of missing out) effect on big business - and what it meant for changes to the way we do business intelligence in the digital era.
Key themes:
Disruption in traditional IT with cloud computing Changing economics and changing business models Rise of Big Data Tech changes to manage Big Data - distributed computing Shift from “current-state” to “next-state” questions Introducing Data Science Challenges - regulatory, data privacy Dangers of data science - over-fitting, interpretation Managing big data projects Data Science MOOCs (massive open online courses), tools and resources
This is the 3rd part of a series of posts on Big Data. Read Part-1 (What is Big Data) and Part-2 (Hadoop).
Traditionally data warehouses have been built with relational databases as backbone. With the new challenges (3Vs) of Big Data, relational databases have been falling short of the requirements of handling
New data types (unstructured data) Extended analytic processing Throughput (TB/hour loading) with immediate query access The industry has turned to Hadoop as a disruptive solution for these very challenges.
As discussed in Part 1 of this series, Hadoop is the foremost among tools being currently used for deriving value out of Big Data. The process of gaining insights from data through Business Intelligence and analytics essentially remains the same. However, with the huge variety, volume and velocity (the 3Vs of Big Data), it’s become necessary to re-think of the data management infrastructure. Hadoop, originally designed to be used with the MapReduce algorithm to solve parallel processing constraints in distributed architectures (e.
You can’t miss all the buzz about Big Data! Over the past few years, the buzz around the cloud and Big Data shaping most of the future of computing, IT and analytics in particular has grown incessantly strong. As with most buzz words, which are then hijacked by marketing to suit their own products’ storylines, but which nonetheless manage to confuse users in business and staff in IT as well, Big Data means several things to several people.
