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.
Multi-model NoSQL database OrientDB supports storing and managing document and graph data sets. Orient Technologies, the company behind OrientDB, announced last month the general availability of version 2.1 of the database.
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.
Snowflake Computing has announced the general availability of their Snowflake Elastic Data Warehouse, a software as a service offering that provides a SQL data warehouse on top of Amazon Web Services.
AWS updated DynamoDB with the ability to publish near real-time notifications of data changes. This new capability – called DynamoDB Streams – spawned two additional features for the NoSQL database-as-a-service: DynamoDB Triggers fire based on specific data changes found in a DynamoDB Stream, and cross-region replication is driven by a DynamoDB Streams-based architecture.
IBM has announced a new web portal called developerWorks Open, bringing together various projects they are open sourcing. The projects cover many domains including Analytics, Cloud, IoT, Mobile, Security, Social, Watson and others. So far, IBM has open sourced about 30 projects, and they plan to increase the number up to 50 by the end of the year, and others may come in the future.
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.
SQL Server 2016 offers a new tool for performance tuning called Query Store. This holds metrics that let you quickly see when an execution plan change has negatively impacted the database.