Optimizing queries in Splunk’s Search Processing Language is similar to optimizing queries in SQL. The two core tenants are the same: Change the physics and reduce the amount of work done. Added to that are two precepts that apply to any distributed query.
A surprisingly common theme at the Splunk Conference is the architectural question, “Should I push, pull, or search in place?”
If you could handle all of the data you need to work with on one machine, then there is no reason to use big data techniques. So clustering is pretty much assumed for any installation larger than a basic proof of concept. In Splunk Enterprise, the most common type of cluster you’ll be dealing with is the Indexer Cluster.
When working with Hadoop, with or without Hunk, there are a number of ways you can accidentally kill performance. While some of the fixes require more hardware, sometimes the problems can be solved simply by changing the way you name your files.
Splunk is jumping into the service-monitoring sector with a new visualization called IT Service Intelligence.
Splunk can now store archived indexes on Hadoop. At the cost of performance, this offers a 75% reduction in storage costs without losing the ability to search the data. And with the new adapters, Hadoop tools such as Hive and Pig can process the Splunk-formatted data.
Splunk opened their big data conference with an emphasis on “making machine data accessible, usable, and valuable to everyone”. This is a shift from their original focus: indexing arbitrary big data sources. Reasonably happy with their ability to process data, they want to ensure that developers, IT staff, and normal people have a way to actually use all of the data their company is collecting.
On August 12, Google announced that its big data processing service has reached general availability. This managed service allows customers to build pipelines that manipulate data prior to being processed by big data solutions. Cloud Dataflow supports both streaming and batch programming in a unified model.
Airbnb recently opensourced Airflow, its own data workflow management framework. Airflow is being used internally at Airbnb to build, monitor and adjust data pipelines. Airflow’s creator, Maxime Beauchemin and Agari’s Data Architect and one of the framework’s early adopters Siddharth Anand discuss about Airflow, where it can be of use and future plans.
Any cloud provider that believes in data gravity is trying to make it easier to collect and store data in its facilities. To make data movement between cloud and on-premises endpoints easier, Microsoft recently announced the general availability of Azure Data Factory (ADF).
At QCon San Francisco, we offer two days of workshops (Nov 19-20). Workshops focus on developing the technical skills that leverage technologies you heard about from our expert practitioners during the conference sessions. Here is a glimpse at some of the experts you can learn from QCon SF ‘15 workshops.
For an organization to be data-driven, it's not enough to just dump mountains of data. That data needs to be accurate and meaningful. Julianna Göbölös-Szabó, data engineer at Prezi shared how they improved the quality of its log data. Their solution involved moving from unstructured to structured data with a lightweight, contract-based approach to nudge all teams in the right direction.
Basho Data Platform supports integration with NoSQL databases like Redis, in-memory analytics, caching, and search. Basho Technologies, the company behind Riak NoSQL database, announced in May, the availability of the data platform that can be used to deploy and manage Big Data, IoT and hybrid cloud applications.
At the recent devopsdays Amsterdam 2015, Patrick Roelke contended that monitoring still has lots of issues. Roelke believes that data science can help by eliminating static thresholds and coalescing information from various data sources into a single metric. The talk included a quick overview of monitoring tools that leverage data science: Kale, Bosun and AnomalyDetection.
Metanautix recently announced the newest version of its product, Quest. Quest allows enterprises to build software defined data marts that can run in virtualized servers. Designed from the ground up with security and auditability in mind, Quest can deal with Big Data workloads and encapsulate it into different logical views, ready for consumption by different departments in enterprise.