By building your own data pipelines, you can populate your existing data warehouse incrementally at a speed for faster analytics and reporting.
Read on to find out how processes like Change Data Capture and workflow orchestration can help you minimize the need for manual intervention for your data warehousing project.
As the number of data sources increases, so do the challenges associated with deriving meaningful insights from your data. Building data pipelines that are robust and suited to handle data from all possible sources is essential to create a single source of truth which can be used for analytics solutions, reporting, and making crucial business decisions.
That said, since incoming data is available in varying formats, issues pertaining to integration are more common than one might think.
When building data pipelines that feed your enterprise data warehouse and allow data to be used by business intelligence BI and visualization platforms, you need to ensure that each part of the process is working perfectly in tandem with the other so that all aspects from ingestion to analytics are taken care of by these data pipelines.
Data orchestration ensures that self-powered data pipelines can be used to perform analyses in near real-time. Without data pipeline automated solution, you run the risk of unnecessary delays and gaps in your analysis which can affect decision making.
Let’s take a closer look at how you can go about building data pipelines and how doing so can improve the efficiency of your data warehouse and analytics architecture.
What Are Data Pipelines and Why Do You Need Them?
Let’s take the example of a chain supermarket company with branches across multiple states.
In order to find out which branches are performing better, you will need to set up data pipelines use-cases to consolidate the sales data of each of these branches into a data warehouse, transform the raw data received from these branches to make it reporting-ready, and connect to business intelligence and data visualization tools to extract key insights about the sales figures of each of these branches and how they can be improved.
When information is received from these branches in near real-time with the help of elaborate data pipelines, you will be able to make any necessary changes to sales and marketing strategies and budgets to maximize profits.
Having accurate and up-to-date information about customer purchasing behaviors and trends will also help you target your audience better and reduce advertising costs.
In situations where you need to integrate data from multiple disparate sources quickly, your decision-makers may not have the time to create new data pipelines from scratch in order to ingest data from these sources without any issues.
As a result, you must ensure that your data pipelines can dynamically adapt and keep up with your changing data sources.
With data pipeline automation, engineers can create a system of data transportation and transformation that dynamically adapts to changing circumstances and reliably transport data from the source to your data warehouse without any manual intervention.
Here’s what a well-made data pipeline will do for you:
- Data extraction from disparate sources and load it to one or more supported destinations
- Transform data to make it reporting and analytics-ready
- Help you identify inconsistencies and errors in your data
- Help you make better business decisions across the board
- Ensure that multiple types of data can be processed without rewriting code
Why is Data Pipeline Automation Important?
Data from Gartner shows that as many as 87 of organizations have low BI and analytics maturity. Without timely access to data, organizations are unable to extract valuable insights from their data at the right time, hence causing delays and gaps in analytics.
By building data pipelines that can be automated, your business can create a system of real-time decision-making so that any problems are identified and rectified as quickly as possible and you can use data to streamline and optimize different tasks to maximize efficiency.
Automated data pipelines can also enhance the flexibility and scalability of your organization by increasing the volume of data that can be processed. This is especially important for insight-driven companies as they are expected to grow as much as 30% annually.
Building efficient and autonomous data pipelines resting at the heart of your analytics architecture, your teams won’t be caught up with writing and rewriting code to create data pipelines that are used to perform repetitive tasks, hence allowing your organization to achieve more with its business intelligence efforts.
How Can You Build a System of Automated Data Pipelines?
Orchestrating and automating your data pipelines is an elaborate process. Since everything from your analytics architecture to your data warehouse are based on data pipelines that ingest and transport data, it is necessary to maintain these data pipelines to ensure that your organization can continue deriving valuable insights from data.
Here are some steps that you can take to automate your data pipelines and maximize efficiency:
Designing functional and efficient workflows should be the first step in automating your data pipelines. Once you’ve decided that, you want to automate the ETL process to load data into your data warehouse, and create a blueprint to decide how different systems will interact with each other.
Is your data up to date?
Does the data need to be transformed before it can be loaded into your data warehouse?
What will happen in case no new data is available from a certain source?
Are there any prerequisites for any of my data pipelines to operate effectively?
You need to ask yourself all of these questions – and more – to ensure that your workflows will continue to operate efficiently. When orchestrating your workflows, you also need to make sure that relevant personnel are notified of any errors or discrepancies in the process with the help of emails or notifications so that they can take prompt actions to prevent any issues from escalating.
In a workflow, this could be as simple as sending automated emails in case some data is not processed the way it needs to be or creating alternative paths in case there are errors in some data.
When building data pipelines to feed data into your data warehouse, you also need to ensure that data is being entered in the correct sequence so that subsequent tasks are not affected due to lack of data.
For instance, if you need to analyze sales data at the end of every week, your data warehouse needs to have that data available from all possible sources at the right time so that your analysis is based on accurate and up-to-date data.
Once you have created your data pipelines , you need to find a way to ensure that you don’t have to copy all of the data to your data warehouse every time there’s a change at the source table. Instead, you need a mechanism to load your data incrementally into your data warehouse.
With incremental data loading, you can ensure that the data in your data warehouse is always up-to-date and accurate. With change data capture, you can improve operational efficiency by propagating changes directly to the data warehouse as soon as thy occur in the source system.
There are a few different types of change data capture that you can use to load data into your data warehouse. The first type of change data capture involves creating a transactional log to capture all changes that occur in your source system.
Since all changes are logged into a separate table, you don’t have to worry about your source data being or operational activities being affected.
In trigger-based change data capture, you can create triggers based on which the instance of a database table can be captured. For instance, if you want to see what a database table looks like after new records are insert, you can apply the AFTER INSERT trigger to capture the right data and store it in a separate table.
The last type of change data capture uses stored procedures that are made available by the database provider. These stored procedures make incremental loading easier by capturing any changes made to the database table and propagating them to a separate table.
Stored procedures are also used for a variety of other purposes including access control and data validation.
Once you have a mechanism to incrementally load data into your data warehouse, you can start performing analysis in near real-time without waiting to collect and load a large batch of data.
Orchestrating and automating your data pipelines and workflows is only the beginning. Once this is done and your workflows are self-powered, you still need to continue to monitor and maintain them to ensure that they are working optimally.
Here, it is important to note that monitoring workflows and data pipelines isn’t the same thing as manually building them from scratch every time your requirements change even a little bit.
Instead, monitoring your jobs from time to time can help you understand if there are any issues with your current system and allow you to make any necessary changes to optimize the process to give your analytics architecture a much-needed boost.
Another key benefit of job monitoring is the amount of control over your data pipelines. In case you receive a notification for an error in a data pipeline, you will easily be able to refer to the job monitoring screen to figure out exactly where the problem lies.
This will allow you to make amends to your data pipelines quickly and efficiently – that too without the need to go through each one of your pipelines to identify where and why the process was interrupted.
Monitoring your jobs from time to time gives your organization a greater degree of control over its data, enabling you to reduce time-to-insight and gain a competitive edge.
The ability to schedule repetitive tasks so that they can be executed without any manual intervention is an important part of data orchestration.
Going back to the supermarket company example we discussed earlier, scheduling data integration processes for tasks such as using your data warehouse for weekly reporting can improve the speed at which the company is able to extract insights and deliver business intelligence.
Since you might need different data pipelines to transport data from your source to the data warehouse at different times, having a job scheduler that can process your data daily, weekly, monthly, or only when certain triggers or conditions are met can streamline the process considerably.
Scheduling and automation increases the mobility of data, ensuring that it can flow within the organization in near real-time.
Once you have self-powered and self-regulating data pipelines, it will be possible for your organization to improve its product and service offerings by making them more personalized, mitigate risks, and support business continuity across the board.
When it comes to your data analytics architecture, it’s essential to have data pipelines that can support your organization’s BI with minimal intervention.
With Astera DW Builder, building data pipelines has never been easier; you can build code-free integration solutions to ETL your data, orchestrate workflows, and feed your data to BI and visualization tools to maximize returns on your data.
With Astera DW Builder, you can:
– Prepare your data for analytics and reporting
– Create reusable, self-powered integration pipelines that will seamlessly transport data from its primary source to visualization tools
– Automate business processes, job scheduling and create a system of notifications and alerts to grant you more control over your data and analytics
– Improve the mobility of your data and increase flexibility by achieving more without expending resources writing and rewriting code to extract key insights
Ready to experience the power of Astera DW Builder firsthand? Get in touch with our team to view a demo or to discuss your specific use case to find out how Astera DW Builder fits in your analytics architecture.