As data-driven infrastructure continues to develop, organizations are burdened with the cost and complication of managing data with traditional tools. This complexity multiplies when businesses aggregate data from various independent sources to enable effective decision-making. Stakeholders and decision-makers need a centralized repository of data to get insight into organizational effectiveness and plan and transform business models. By using reliable analytics and visualization tools like Tableau, businesses can deploy robust data architecture.  

But implementing Tableau alone is not enough! 

A systematic approach is essential to efficiently capture and process large volumes of data, which includes implementing well-designed data infrastructure and data-driven solutions. That’s why building data pipelines with Tableau is important. This way, companies can strategically gather, transform, and extract value from business data and remain competitive.  

Architecting Data Pipelines with Skilled Tableau Consultants 

Though Tableau offers guides and modules to build pipelines, businesses need the expertise of skilled consultants. It’s because the data pipeline development process includes a series of data processing steps that allow data to shift from multiple data sources to an endpoint (e.g., data warehouse or data lake).  In short, Tableau consultants process and transform each data source before it is combined and connected to a repository. The transformation ensures proper data uniformity and integration, and in this stage, consultants perform cleaning, filtering, masking, and validation.  

It is a common practice of Tableau consulting professionals to use both well-defined business requirements and exploratory data analysis to determine the type of data processing performed by a data pipeline. The processed data can be the input for analytics activities and data-driven applications. 

Overall, Tableau consultants simplify the movement, alteration, and processing of data within data pipelines reliably. This helps businesses make use of their data and get insights to make tactical decisions. 

Key Data Pipeline Components that Consultants Focus on 

The components to be included in a data pipeline vary based on the specific needs of the businesses, but some standard components that Tableau consultants incorporate during the development process include: 

• Data Storage – Additional data storage systems are being employed by consultants. This is where data is stored after the processing phase and will act as a backup to the data pipeline. Some of the commonly used data storage systems are Apache Kafka and Hadoop. 

• Data Replication – This component takes a copy of the filtered and cleansed data before delivering it to the endpoint. The duration of any replication depends on the data processing strategy. 

• Data Sinks – Sinks act as the final termini of a data pipeline. Examples of data sinks include warehouses, databases, and data lakes. By integrating data sinks with a data pipeline, Tableau consultants allow businesses to store their data for analysis and reporting purposes. Administrators and analysts can utilize this data for Business Intelligence and other initiatives. 

• Dataflow – Tableau consultants also determine the movement of data within a pipeline, starting from its point of derivation to its point of destination and any alterations applied to it. Three of the most widely used approaches by consultants are ELT, ETL, and converse ETL. 

• Scheduling Systems - These systems are used by consultants to automate recurring or regular tasks that run at specific intervals within a pipeline. Some of the repetitive tasks in a data pipeline include data processing and data ingestion. 

• Monitoring Systems – Such systems are incorporated by consultants to monitor the status of tasks, trigger alerts, workflows, and other actions when disputes arise. This can include pipeline performance, monitoring information quality, and resource consumption. 

Steps Followed by Tableau Consultants in Creating Data Pipelines 

1. Gain a Clear Understanding of Objectives 

At first, Tableau consultants assess the objectives of businesses behind building a data pipeline. Consequently, they look for available data resources, time/budget constraints, existing data metrics, and end-users.  

For example, when organizations need data for creating Machine Learning models, consultants would build a data pipeline that generates datasets. The consultants also deal with tasks such as splitting data into training and testing sets, feature engineering, and data normalization.  

Similarly, some businesses expect to transform their databases into live feeds and expedite data delivery. In such cases, Tableau consultants build and launch streaming data pipelines. However, when organizations need real-time data but not constant data streaming, consultants can develop Lambda pipelines with Tableau. This type of pipeline is a combination of streaming and batch pipelines in one environment. It is ideal when business users want data based on triggers or specific events. Therefore, by obtaining a comprehensive understanding of the business’s goal, Tableau consultancy firms decide the type of pipeline development. 

2. Workflow Configuration 

Workflow configuration helps in categorizing the order of processes in the data pipeline and Tableau consultants make sure that each workflow is interdependent. Consultants follow two types of workflow configuration models (business-related or technical). Business-based pipeline configuration includes cross-evaluating data from one source against another to maintain integrity before merging. Technical configuration includes aspects like retaining data after it has been gathered from sources, going through further authentication steps, and then being transferred to a destination. All these will influence the execution of data pipeline architecture. 

Tableau consultants use workflow management solution ‘Blueprint’ to make data pipeline creation process less complex. This allows business users and data engineers to view and manage data workflows with greater transparency. Moreover, workflow configuration automatically resolves dependencies and precisely organizes the pipeline activities. 

3. Define the Data Source and Destination 

It’s the responsibility of Tableau consultants to determine from where the pipeline should extract data and where it needs to be transferred. It could be a database, a file on a server, a mobile/web API, or another data source. In this stage, consultants discuss with business stakeholders and in-house professionals to regulate the data flow based on their requirements. 

4. Executing the Dataflow  

In this stage, Tableau consultants implement the ETL process. For instance, 

• Extract the data – Once businesses determine from where the pipeline should get data, consultants set the extract conditions and integrate them with desired databases. The extract conditions excerpt data from databases such as Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), and other apps.  

• Pre-process the data as per business needs -  – Typically , extracted data is in a raw format that is inappropriate for analysis or processing. Tableau consultants change it into a more suitable format as per the business standards and requirements. This involves aggregating, cleaning, evaluating for redundancy, and merging it with data from other sources.  

• Loading the data - After excerpting data from sources and implementing any required transformations, the consultants load them into a centralized repository. This can be either data warehouses like Snowflake and Redshift or physical databases. 

5. Determine Scheduling 

A data pipeline is often a programmed mechanism that operates on a pre-determined scheduling system. Whether it’s hourly, daily, or at custom frequency, Tableau consultants establish a schedule for the pipeline to function as per the organization’s needs. 

6. Set up Monitoring Framework & Make Changes 

Experts from a recognized Tableau professional services provider monitor the data pipeline regularly to ensure information is correct and processed effectively. The experts also set up alerts to notify during possible downtime scenarios (e.g., network congestion) that occur within the data pipeline. 

Based on the outcomes of monitoring, consultants can also introduce a few updates to accommodate any additional data ingestion techniques or managing modifications. This might include altering the transformation stages, reviewing the schedule, or shifting the data’s endpoint. 

7. Auditing and Governance 

After architecting a data pipeline, Tableau consultants perform periodic pipeline audits. Conduct data validity, accuracy, and integrity checks and make sure that the data adheres to relevant standards or regulations. Other techniques followed by consultants during auditing include manual data inspection, programmed checks using tools or scripts, and data quality tracking systems. Consultants can even determine who will access the final prepared data after the loading stage of data pipeline development.  

Summing Up 

With entrepreneurs and organizations relying on data insights to make tactical decisions, it is crucial to build data pipelines with Tableau and access genuine business information. Getting data from multiple sources into a single pipeline can be a time-intensive and resource-consuming task. However, with the help of Tableau consultants, businesses can design and launch personalized pipelines and democratize data analytics and visualization.