International Big Data Analytics Conference 2016
by Mike Ferguson, Rick van der Lans, Tim Seears, Vladimir Bacvanski
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Description
Big Data Analytics has without doubt become mainstream in 2016. Companies are initiating data science projects on Big Data platforms such as Hadoop and Apache Spark, offloading ETL processing to Hadoop, and archiving to Hadoop. Also more capability being put in the hands of business analysts and data scientists using self-service tools for data preparation, analytics or both. Apache Spark has become even more of a force in the analytics market with many different information management and analytical tools now integrated with Spark and using Spark Streaming, Spark SQL, MLlib, and GraphX both on-premises and in the Cloud. New Spark analytics libraries have also emerged.
Also the data deluge continues to grow. More and more data is coming into the enterprise and without a doubt, information management and governance in a Big Data environment is turning into a huge issue. Therefore the emphasis has shifted to cataloging and classification of data as a way to improve data governance and applying policies according to how the data has been classified. In addition, centralized Data Lakes are giving way to distributed Big Data environments with multiple relational and NoSQL data stores now being used in a hybrid Cloud and on-premises computing environment. Big Data governance, including security and privacy, is therefore now paramount especially now that the European Parliament has issued new data privacy legislation which affects all member countries. The clock is now ticking and we have until May 2018 to guarantee compliance with this new legislation.
In addition, the latency of data is being pushed closer to real-time as companies sell smart products and leverage the data generated by the Internet of Things. This means that for many companies, they will for the first time be looking at the installation and deployment of streaming analytics platforms and at technologies like Kafka, Akka, Spark Streaming and Flink to manage very high velocity data ingestion and continuous data processing. The need to scale has always been an issue but now this issue really is on a new level with fault-tolerance also needing to be addressed.
It is not surprising therefore that the Big Data market continues to advance with new emerging technologies like Apache Beam, Apache Arrow, Apache Eagle and Apache Millwheel. Also how does all of this integrate with existing analytical environments? Do architectures need change? Should we now focus on the Logical Data Warehouse? Also how will all of this help improve business?
This Conference aims to provide ‘how to’ sessions on Big Data and analytics to show how to exploit the latest scalable technology and how to create a data driven enterprise. It provides sessions on advanced analytics, stream processing, Internet of Things, core Big Data technologies and how to extend information management and data governance to encompass Big Data security, privacy, self-service data integration, and governance of a Data Lake. It also for the first time includes real case studies of how Big Data is being implemented in practice by many companies in the Italian and wider European marketplace.
SPONSORS
Main Topics
- Big Data Concepts and Core Technologies
- Data Modeling for NoSQL Databases and Big Data
- Predictive and Advanced Analytics Using Data Mining Tools and Apache Spark
- Extending the Logical Data Warehouse with Big Data
- Analyzing Big Data in a Cloud Computing Environment
- Emerging Technologies in Big Data, and Avoiding the Pitfalls
- Fast Data: Big Data Analytics Using Stream Processing
- Data Security and Privacy in a Big Data Environment