IBM Data Architect with SQL, Spark & Kafka Certificat Professionnel

Describe data, databases, relational databases, and cloud databases.

Describe information and data models, relational databases, and relational model concepts (including schemas and tables). 

Explain an Entity Relationship Diagram and design a relational database for a specific use case.

Develop a working knowledge of popular DBMSes including MySQL, PostgreSQL, and IBM DB2

Analyze data within a database using SQL.

Create a relational database on Cloud and work with tables.

Write SQL statements including SELECT, INSERT, UPDATE, and DELETE.

Build more powerful queries with advanced SQL techniques like views, transactions, stored procedures and joins.

Describe the Linux architecture and common Linux distributions and update and install software on a Linux system.

Perform common informational, file, content, navigational, compression, and networking commands in Bash shell.

Develop shell scripts using Linux commands, environment variables, pipes, and filters.

Schedule cron jobs in Linux with crontab and explain the cron syntax. 

Create, query, and configure databases and access and build system objects such as tables.

Perform basic database management including backing up and restoring databases as well as managing user roles and permissions. 

Monitor and optimize important aspects of database performance. 

Troubleshoot database issues such as connectivity, login, and configuration and automate functions such as reports, notifications, and alerts. 

Job-ready data warehousing skills in just 6 weeks, supported by practical experience and an IBM credential.

Design and populate a data warehouse, and model and query data using CUBE, ROLLUP, and materialized views.

Identify popular data analytics and business intelligence tools and vendors and create data visualizations using IBM Cognos Analytics.

How to design and load data into a data warehouse, write aggregation queries, create materialized query tables, and create an analytics dashboard.

Differentiate among the four main categories of NoSQL repositories.

Describe the characteristics, features, benefits, limitations, and applications of the more popular Big Data processing tools.

Perform common tasks using MongoDB tasks including create, read, update, and delete (CRUD) operations.

Execute keyspace, table, and CRUD operations in Cassandra.

Describe and contrast Extract, Transform, Load (ETL) processes and Extract, Load, Transform (ELT) processes.

Explain batch vs concurrent modes of execution.

Implement ETL workflow through bash and Python functions.

Describe data pipeline components, processes, tools, and technologies.

Explain the impact of big data, including use cases, tools, and processing methods.

Describe Apache Hadoop architecture, ecosystem, practices, and user-related applications, including Hive, HDFS, HBase, Spark, and MapReduce.

Apply Spark programming basics, including parallel programming basics for DataFrames, data sets, and Spark SQL.

Use Spark’s RDDs and data sets, optimize Spark SQL using Catalyst and Tungsten, and use Spark’s development and runtime environment options.

Build valuable applied data storage, integration, and migration skills employers need. 

Gain hands-on experience using industry-specific data tools.

Demonstrate you understand data-related best practices and can apply methodologies through industry-standard processes. 

Showcase your ability to solve problems related to data processes that you can talk about in interviews.

Develop and implement effective data privacy and security strategies.

Understand and apply security measures to protect and govern organizational data.

Conduct risk assessments and implement appropriate risk management practices.

Navigate and comply with relevant legal and regulatory compliance requirements.

Importance, benefits, and core components of Enterprise Data Architecture (EDA) and popular data architecture frameworks.

How to design and implement Enterprise Data Architectures for specific use cases.

How to develop and implement policies and procedures, such as data retention policies and operational standards.

How to plan and execute data system migrations and modernizations.

Gain hands-on experience working with data architecture that you can showcase in your portfolio and talk about in interviews.

Analyze and assess existing data architecture in alignment with business objectives.

Implement data migration and integration solutions, including building data pipelines.

Apply data governance and security protocols, ensuring compliance and data protection.