There are two parts to analytics powered by artificial intelligence (AI) — the model and the data. But oftentimes data practitioners are so busy improving their model building skills that they put little emphasis on how best to get the data in the initial stages. The need to train models with holistic, trusted data is where many projects go off the rails. How well you control your data and data flow will determine how well you can avoid the pitfalls of AI based on poor quality data.
The Optimal Path for Creating a Data Pipeline
There is no one way to create your data pipeline. But there could be an “optimal path.” When you follow it, you can get maximum performance from your data pipeline at minimal cost. But how do you get there?
There are two ways to reach this goal. One way is to experiment with data integration patterns, processing engines, storage, queries, data, latency, tools and technologies. The other way is to let AI recommend that for you depending on your use case.
But for AI to learn from your environment, it needs data from the past performance of your pipelines. So, to become an expert in data integration, it’s helpful to experiment with different tools and techniques and learn what works, what doesn’t work and what works better.
And if data integration is not your core competency, then rely on AI, implement its recommendations, automate where possible and save time for things that interest you. AI helps you to collapse the complex backend and interact with the tool as little as possible — saving you time and effort.
Designing a Data Pipeline From Scratch
Creating a data pipeline from scratch enables you to access data in a programmatic way. The effort you put into design can save you from the recurring heartburn that follows if you don’t. As they say, measure twice, cut once.
While the data pipeline–creating process remains the same, when you are integrating data for AI you need to look into the scalability and availability of trusted data. And both capabilities are made easier with AI. We will explain as we go through each of the steps below:
1. Define the project: it’s very important to understand what you want to achieve with integration. Is it a unified view of data from different sources? Is it to run real-time analytics, optimize business processes, feed data to AI and machine learning models? Is it simple replication or migration of data from on-premises? Or is it actionable insights from multi-contextual data?
Once you define the project, you should determine what your priorities are. Do you plan to expand the project to enterprise scale? Or is just a small departmental project?
How AI can assist: AI can read from your past project data and surface the correlations among different elements. This serves to highlight the dependencies required for success.
2. Identify sources and target: Pick your data sources and target. Make sure you have access to pull and push data to those applications, systems and services.
How AI can assist: AI can recommend data sources based on how you define the data needed. It can suggest best practices in terms of data quality and transformation rules you should apply to get that data in a standard format. It can point to an existing data product that you can consume, saving you a lot of effort and worry.
AI can learn the context and suggest content based on your requirement. For example, imagine you are struggling to connect to Snowflake. An in-platform recommended video can provide guidance on what you need. You might then realize that you need a private key file and password to get access.
3. Access the data: Define the data in terms of the four “Vs” — volume, velocity, variety and veracity. The volume of data that has to be processed will determine how scalable your tool should be. The latency that your data pipeline can handle will decide the integration technique you should opt for. A versatile tool should be able to handle virtually all data types — structured data such as relational databases; semi-structured data such as JSON or XML files; and unstructured data such as audio, video and text based flat files.
If you want to work with dummy data, figure out a source. There is dummy data available in communities and forums.
How AI can assist: There are AI-based recommendation engines that can suggest the level of parallel processing needed by looking into the volume of data and amount of money you want to spend. This enables you to optimize your data workload both in terms of cost and performance with AI.
A simple example of AI in this situation would be auto-scaling, in which your unpredictable workload can be managed with the automatic scale-in and scale-out of your infrastructure.
4. Decide on an integration process: Now that you know the type of data available and the format of data needed at target, you can decide on the process of extracting, transforming and loading the data. If you don’t need to transform the data, you can opt for high-speed data loading or data ingestion. Data migration, replication or backup projects can be handled using an ingestion tool.
If you need to sync application to application, opt for application integration. On the other hand, if you are creating a data warehouse, you might want to transform the data and standardize it before you put it in a well-defined schema in the data warehouse using an ETL process.
If you need to transform data on a data lake or warehouse, you can use an ELT process, in which you don’t move the data but instead push down the code to process the data at the source/target. There are also different integration techniques or frameworks such as data mesh, data fabric, data virtualization, a data hub and data federation. But for beginners, pick between ingestion, ELT and ETL processes to get started.
How AI can assist: A wizard-driven experience makes designing complex data pipelines easy. AI acts as a co-pilot and automatically suggests process, technology, engine, transformation and expressions at each step of data mapping. AI can auto fill components in a data mapping depending on the learnings. An AI-based optimizer would decide the best processing engine.
5. Define data quality and transformation requirements: The enrichments that your data requires will determine how advanced a data integration tool you require. The basic tools lack complex transformation rules and the ability to automate the whole flow.
How AI can assist: AI makes it easy to implement data quality best practices. You can automate data quality rules to improve the accuracy and completeness of the data used. Mapplet recommendations can help you reuse a set of transformations for a certain data set, boosting productivity.
6. Address data security and privacy: If you are aiming for enterprise-scale data pipelines, being aware of security, privacy, compliance and governance policies will get you ready for prime time. Help ensure transparency with end-to-end data lineage. Implement user access and control policies to help make sure data gets into the hands of the right users.
How AI can assist: With AI, implementing and validating policies is scalable and easy. You can standardize across the enterprise, and the machine learning can increase with every user interaction. It’s easier to upgrade policies and build uniformity and transparency into the system.
7. Test: Testing data pipelines helps ensure better data quality and faster release cycles.
How AI can assist: Automate testing at every phase. It helps you deliver on your service level agreements (SLAs) without burdening your team with mundane tasks. Apply automated data testing at every step, reduce error rates and see fewer bugs escape to production.
8. Monitor and optimize: Monitor how data is consumed and protected and how it complies with policies and regulations. Provide visibility into the health of data at every stage of the pipeline and identify the impact and root cause of issues, so preventative and remedial action can be taken. Optimize availability, performance and capacity in the most cost-effective and efficient manner across on-premises, hybrid and multi-cloud.
How AI can assist: AI can monitor data pipelines and predict issues that might happen if not previously resolved. You can pre-set a chain of actions to troubleshoot a problem or anomaly. With AI, the system can automatically and intelligently escalate a problem for human intervention where needed. For example, if an integration fails, it can retry after a certain interval. In cases where an application is unresponsive for a prolonged period of time, it can create a Jira issue and route it to the appropriate Slack channel.
Discover the benefits you can derive from AI-powered data integration. Start small and experiment with your data pipeline with Cloud Data Integration-Free. The billing is on us. There is no limit to the length of time you can use the solution as long as you don’t exceed 20M rows / 10 compute hours per month.
And when in doubt, reach out to the developers of Cloud Data Integration-Free through our dedicated community. See you there!