Salesforce Data-Architect Exam With Confidence Using Practice Dumps

 Engaging with executive sponsorship to assess enterprise data strategy and goals, and engaging with business units and IT to assess current operational systems and data models are two first steps that would be appropriate for UC to initiate an assessment of data architecture. These steps will help to understand the current state of data management, the business needs and expectations, and the gaps and opportunities for improvement. Engaging with IT program managers to assess current velocity of projects in the pipeline, and engaging with database administrators to assess current database performance metrics are not relevant steps for assessing data architecture, as they are more related to project management and technical performance.

The three key steps that a data architect should take when merging data from multiple systems into Salesforce are:

Analyze each system’s data model and perform gap analysis. This step involves understanding the structure and meaning of the data in each system, identifying the common and unique data elements, and mapping the data fields between the systems. This step also involves assessing the quality and consistency of the data, and identifying any data cleansing or transformation needs.

Utilize an ETL tool to merge, transform, and de-duplicate data. This step involves using an ETL tool to connect to the source systems, extract the data, apply any data transformations or validations, and load the data into Salesforce. This step also involves applying de-duplication rules or algorithms to avoid creating duplicate records in Salesforce.

Work with stakeholders to define record and field survivorship rules. This step involves collaborating with the business users and owners of the data to determine which records and fields should be retained or overwritten in case of conflicts or discrepancies. This step also involves defining the criteria and logic for record and field survivorship, and implementing them in the ETL tool or in Salesforce.

Creating new fields to store additional values from all the systems is not a key step, but rather a possible outcome of the gap analysis. It may not be necessary or desirable to create new fields for every value from every system, as it may result in redundant or irrelevant data. Installing a 3rd party AppExchange tool to handle the merger is not a key step, but rather a possible option for choosing an ETL tool. It may not be the best option depending on the requirements, budget, and preferences of the organization.

The best solution to extract 50 million records from a custom object everyday from Salesforce org without facing query timeout issues is to use an ETL tool for extraction of records. ETL stands for extract, transform, and load, and it refers to a process of moving data from one system to another. An ETL tool is a software application that can connect to various data sources, perform data transformations, and load data into a target destination. ETL tools can handle large volumes of data efficiently and reliably, and they often provide features such as scheduling, monitoring, error handling, and logging5. Using a custom auto number and formula field and use that to chunk records while extracting data is a possible workaround, but it requires creating additional fields and writing complex queries. The REST API can extract data as it automatically chunks records by 200, but it has some limitations, such as a maximum of 50 million records per query job6. Asking SF support to increase the query timeout value is not feasible because query timeout values are not configurable