This Oracle Database 11g: (SQL Tuning Workshop R2) course helps SQL developers, DBAs and database developers in tuning and identifying ineffective SQL statements. You will explore investigative techniques to disclose different levels of insight concerning how the Oracle database performs the SQL statements; this assists you to identify the main reason for the ineffective SQL statements.
This course helps you to enhanced skills in relational database management as you study the method to efficiently utilize SQL commands in your business data. These functions will assist you to manipulate and query data into the database. Additionally, you will learn how to use dictionary views to recover metadata and make reports regarding their schema objects.
Professional trainers will also support you to learn how the optimizer selects the path. You will also study how to control the optimizer to assure the best approach is used.
This training course also incorporates the Automatic SQL Tuning resources and tools accessible in the Automatic Workload Repository. Moreover, they benefit from trace files, bind variables, and various sorts of indexes.
Note: This training course in accordance with Oracle Database 11g R 2.
The Oracle Developer can make an average salary of $106,225 per annum.
|Subjects||IT Ops & Management|
|Learning Style||Virtual Classroom|
|Course Duration||3 Days|
Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.