The landscape of employment is changing. The job opportunities in the field of data science is surging onwards, that too at a robust rate. Continue reading →
Do you know why several organizations face problems while implementing Big Data? Still wondering? The reason is lack of poor or non-existent data management strategies.
Proper technology systems need to be adopted. Without procedural flows, data is impossible to be analysed or delivered appropriately. However, before we delve deeper into making a plan to introduce data management strategies into the business, we should pay enough attention to the systems and technologies we are thinking to launch, along with the number of improvements to be made.
Big Data is ruling the tech world. Here are few types of tech that needs to be a part of a successful data management strategy:
Common data mining tools are R, SAS and KXEN.
More consistent, Automated ETL is used to extract, transform and load data.
They are efficient in offering a protective layer of security and quality assurance by doing a proper problem diagnosis and monitoring critical environments.
BI and Reporting Analytics
Turn data into insights, with BI and Reporting Analytics. It is very vital that data go to the right people and of course in the right manner. If that doesn’t happen, organizations suffer incessantly.
Analytics is a huge branch of study, starting from customer acquisition data, tracking details to intriguing user-friendly interfaces and product life cycle.
For More Details, Read The Full Blog Here:
Understanding The Core Components of Data Management
For regular updates on SAS predictive modeling training Pune and Gurgaon, and other developmental interactive SAS certification for predictive modelling courses, reach us at DexLab Analytics.
The ‘trending’ topic this season is Data Processing. The statistics attached with this blog depicts that respondents have mainly voted for NoSQL and SQL databases. The opinions of the respondents have conferred the title of ‘most engaging’ to NoSQL database, confirming the second position with a 74.8%.
The survey declares the PostgreSQL as the confirmed winner, where 25.3 % have proclaimed it to be ‘very interesting’ and 37.7 % have confined within ‘interesting’.
Also read: Top Databases of 2017 to Watch Out For
- Elasticsearch declared runner-up with an overall 59%.
- The amalgamation of Lucene and Solr roaring with 43.8%
- More interest devoured in Apache Spark with 3%
- Hadoop scoring a meager of 8%
Next, it unveils that the US respondents have mainly opted for Elasticsearch to PostgreSQL, and Oracle have failed to evoke any interest in the mind of US respondents. However, the picture is completely opposite for the European respondents.
Also read: Data Analytics for the Big Screen
The ending note states that it is high time we realize that the dire need of the hour is data storage and processing. This conclusion is supported by the fact that so many respondents have invested their valuable time in the survey and clearly shows that database is here to stay.