Currently more and more data is generated by businesses and being collected. Integration with Big Data allows you to bring all of the different types of data in to one place and answer completely new business questions. We can integrate your big data with existing applications and reports based on Data Warehousing environment with reliable Oracle authorization mechanism. We can help you collect data, clean it, prepare it and transform to make it available in a existing it solutions.
ORACLE BIG DATA CONNECTORS
Do you want to have access to data stored in Hadoop directly from Oracle?
Do you want to apply unified Oracle security? Do you want to offload your Oracle database?
Do you want to query data stored in Hadoop efficiently?
If the answer is yes, Oracle Big Data SQL should be a great choice for you.
Thanks to Oracle Big Data SQL, you can treat the Oracle Database as a single SQL interface for Big Data. Data stored in Hadoop or the NoSQL Database is queried in the same way as all other data in the Oracle Database using the rich query engine. You can seamlessly use the same applications and Oracle skills utilizing data stored in Hadoop.
OFFLOADING ORACLE PROCESSES WITH BIG DATA USING APACHE SPARK
You can minimize database CPU consumption during the ETL process by offload data pre-processing to Hadoop. Raw data could be stored on the Hadoop layer, all heavy processing can be done on the Big Data layer and return already processed data to the database. On the Hadoop layer, transformation of data to the Oracle DB format can also be performed.
We can integrate your big data with existing applications and reports based on the Data Warehousing environment with a reliable Oracle authorization mechanism. We can help you collect data, clean it, prepare it and transform it in order to make it available in existing solutions or expose data using Oracle APEX.
USUALLY, OUR CLIENTS FOLLOW ONE OF THE FOLLOWING PATTERNS:
DATA SCIENCE / AI SANDBOX IN BIG DATA
Data science and advanced analytics are often the first business departments to be moved into Big Data solutions. Analytical workload requires significant computing resources growing rapidly with data volumes. Results are a direct derivative of processing power. Easy and affordable scalable Big Data platforms are the perfect choice. Data scientists are very eager to experiment and test new solutions. This is their daily work since new analytical algorithms are developed very frequently. They need a flexible environment where they can play with data and technologies. It does not need to be as stable/managed as DWH. Hence the idea of an analytical sandbox appeared.
The diagram below presents the reference architecture:
WHAT ARE THE MOST COMMON ISSUES WE ARE DEALING WITH IN THE CASE OF OUR CUSTOMERS’ SYSTEMS?