We are the experience demanded for processing massive amounts of data from web logs, banking transactions, purchase transactions or signal data. We know how to save, process, analyze, predict future behavior and so much more, and can guide you through the hundreds of terabytes available. Using the most up-to-date information and records, we implement up-to-real-time solutions to harness your data and drive innovation.
Move your business forward and create value with Big Data platforms. Close cooperation between your IT department and the business itself is a must; it is an unavoidable fact if Big Data technologies are to be efficiently deployed. How the company can benefit from richer data sources and where this data can succeed must be clarified and defined. IT departments will be the movers and shakers behind the innovations leading to new infrastructure solutions as you deal with the practical concerns of Big Data technology implementations.
The Hadoop market is forecast to grow at a compound annual growth rate (CAGR) of 58% and to surpass $16 billion by 2020.
Log analysis and infrastructure monitoring
A frequent starting point when implementing Hadoop technologies is log analysis. Its popular use in this area stems, in particular, from its low costs for data storing and its speed in processing tremendous volumes of data.
The volume of log data is increasing rapidly for large organizations and analyzing them with traditional related databases and BI tools is demanding and expensive. Until recently, log data was inevitably stored for the shortest time possible making it impractical for organizations to work with this resource.
Hadoop technologies are the game-changer the industry has been waiting for. Hadoop clusters use specialized tools to integrate multiple data sources and collect data in a Big Data platform in near real-time via streaming, an innovative approach that continuously analyzes and transforms data before storing it on a disk.
Stored logs can be used for infrastructure monitoring and incident prevention, for accessing evaluation in real-time for IT system security, fraud detection or an analysis of web traffic.
Data warehouse extension with new data sources
How can you collect non-structured data from social networks, e-mails, transcripts of call center calls, clickstream log data related to web site traffic or from other sources at the lowest price? It can be done affordably with Hadoop platform integration tools. Start analyzing never-before-seen data and contextualize them with existing data to discover numerous new competitive advantages to upgrade your sales or services processes.
Cluster installation and set-up
Developing Big Data applications in-house requires extra capacity and experienced specialists to ensure efficient cluster installation such as recommendations for capacity dimensioning of purchased servers before successfully installing, setting it up and piloting the launch, so do not hesitate to contact us. Adastra can also advise you on designing the correct cluster set-up in Microsoft Azure HDInsight and Amazon Elastic MapReduce (Amazon EMR) cloud environments.
Optimization of DWH workload and sandboxing
When developing a new corporate application to process data from a data warehouse, the data warehouse can be overloaded with queries that have no added sales value resulting in resources being used inefficiently.
The Hadoop platform can replicate sections of data from the DWH and enable it to serve as a sandbox of organizational data for developers where ad-hoc clustered data for querying is accessible and a space for Proof of Concept is created to prepare projects in the DWH. Adastra recommends you „train“ on data in volumes of hundreds of GB which Hadoop can easily process unlike a regular data warehouse. Data warehouse resources are subsequently saved and support the level of innovation in your organization.
Data archiving – Offloading cold data
Hadoop can also serve as an archiving solution to reduce the workload of the data warehouse. Price is a major advantage as it ranges from only 1,000 – 3,000 EUR per TB of stored data. Such a low price is possible as Hadoop uses lower cost commodity servers. High availability of data is ensured through sophisticated protective strategies against failure. Data replication is central to this concept as it effectively prevents data loss by replicating the data up to three times if any of the servers in the cluster fail.
Another significant advantage comes through the ability to jointly query over data in both the data warehouse and the Hadoop cluster at the same time courtesy of the Apache Toad in an approach known as SQL push down which minimizes the need of data transfers between the data warehouse and Hadoop cluster.
If your organization manages a large number of documents and it is difficult to find requested information within the volume, the Hadoop cluster can upload data to a distributed file system which can contain an almost infinite number of documents of any format.
You can then enable full-text searching of these documents in real-time via ElasticSearch which gains performance benefits of the Apache Hadoop platform – high availability and a searching algorithm with distributed processing. Communication with this tool is easy thanks to a RESTful API which poses few problems to integrate into enterprise applications in order to gain a tangible competitive advantage.
At work, you can use a familiar, user-friendly graphical interface to take advantage of a wide range of data processing features including data integration, manipulation, and transformation, not to mention shared metadata and rich data integration. Ataccama BDE replaces specialized ETL technologies and accommodates the key Big Data attractions of massive parallel processing, scale-out options, fault tolerance, memory management, all of which is available to anyone who needs to profile, map, model, process, transform, cleanse, enrich, and integrate Big Data. Spend less time integrating and more time putting Big Data to business use.
Ataccama DBE benefits (Advantages of Ataccama BDE):