The application of analytics in the industry is widespread and diverse. From connecting all elements of a technological ecosystem to learning from and adapting to new events to automating and optimizing processes, these use cases are all about supporting the people behind every business, aiding their productivity, and unlocking insights that drive faster business outcomes.
As a society, we are increasingly seeing analytics as the fuel that drives developing economic and social ecosystems that have the potential to alter our economy and the way we live, work, and play. Data is at the heart of how we operate our companies, create organizations, and govern our personal and professional lives. Whether via software programs, social media links, mobile communications, various digital services, or even the underlying infrastructure that enables all of this, almost every encounter creates data. When you multiply those interactions by an ever-increasing number of linked individuals, devices, and contact points, the scale becomes overwhelming—and it’s just getting more significant.
All of this data has enormous potential, but putting it to use may be tricky. The good news is that today’s inexpensive and elastic cloud services are providing new data management options—as well as unique needs for building data pipelines to gather and use all of this data. You may collect years of historical data and progressively reveal patterns and insights using well-built pipelines. You could leverage continuous data streaming to enable real-time analytics. There’s a lot more.
A data pipeline is a series of steps that take raw data from many sources and transport it to a storage and analysis location. Filtering and features that enable robustness against failure may also be included in a pipeline. After absorbing data from sources, the data may be kept in a central queue before being subjected to further validations and eventually being dumped into a destination, as an example of technological dependence. Data visualization and data verification that must be cross-verified from one source to another for correctness before aggregation is an example of a business dependence.
Process of putting data into a pipeline
A data pipeline is a collection of operations that move data from one database to another. Consider any pipe that accepts something from a source and transports it to a destination to understand how a data pipeline works. The business use case and the target determine what happens to the data along the journey. A data pipeline may be as basic as extracting and loading data, or it can be structured to handle data in a more complex way, such as training datasets for machine learning.
Relational databases and data from SaaS apps are examples of data sources. A push mechanism, an API call, a replication engine that pulls data at regular intervals, or a webhook are all common ways for pipelines to ingest raw data from multiple sources. Data may also be synced in real-time or at predetermined intervals.
A data repository, such as an on-premises or cloud-based data warehouse, a data lake, or a data mart, or a BI or analytics application, may be used as a destination.
Data standardization, sorting, deduplication, validation, and verification are examples of transformation operations. The ultimate objective is to make it feasible to examine the data.
Batch processing, in which source data is gathered on a regular basis and transferred to the destination system, and stream processing, in which data is obtained, altered, and loaded as soon as it is produced, are the two data, intake models.
Workflow is the management of processes’ sequencing and dependencies. Dependencies in the workflow may be either technical or business-related.
To maintain data integrity while building data pipelines, a monitoring component is a must. Network congestion or an offline source or destination are examples of probable failure situations. The pipeline must have a mechanism that warns administrators about such circumstances.
Unfortunately, not all data pipelines are capable of meeting today’s business requirements. When designing your architecture and selecting your data platform and processing capabilities, you must make cautious decisions. Pipelines with constraints in the underlying systems that store and process data should be avoided since they might add unneeded complexity to BI and data science efforts. For example, you may need to take additional steps to transform raw data to Parquet because your system demands it. Perhaps your processing systems can’t handle semi-structured data in its native format, such as JSON.
So, how can you keep your data pipelines efficient and dependable while avoiding excessive processing?
5 ways to build reliable data pipelines effectively
Take a close look at all of your data pipelines
Do some of them exist just to improve the physical arrangement of your data while bringing no value to your business? If this is the case, consider if there is a better, more straightforward approach to handle and manage your data.
Consider your changing data requirements
Assess your present and future requirements honestly, and then compare them to the reality of what your current architecture and data processing engine can give. Look for ways to simplify, and don’t be held back by outdated technologies.
Discover hidden layers of intricacy
In your data stack, how many separate services are you running? How simple is it to get data from these different services? Do your data pipelines have to operate around distinct data silos’ boundaries? To maintain appropriate data protection, security, and governance, do you have to duplicate efforts or operate several data management utilities? Determine which procedures need an additional step (or two) and what it would take to make them simpler. Keep in mind that scale is thwarted by complexity.
Take a close look at your expenses
Do you have a usage-based business model for your core data pipeline services? Is it tough to build new pipelines from the ground up, and does it need specialized knowledge? What percentage of your technical team’s effort is spent manually tweaking these systems? Make sure you factor in the expense of managing and governing your data and data pipelines.
Develop pipelines that offer value
As part of the analytics process, pipelines established just to transform data so that systems can work with it do not produce insight (or contribute value). Whether a data transformation is performed as part of a data pipeline or as part of a query operation, the logic to join, group, aggregate, and filter that data is fundamentally the same. When users send identical or similar queries frequently, moving these calculations “upstream” in the pipeline improves speed and amortizes processing costs. As part of the analytics process, look for methods to generate insight.
Getting a Data Pipeline Up and Running
Before you attempt to construct or implement a data pipeline, you need to know your business goals, what data sources and destinations you’ll be using, and what technologies you’ll need. Setting up a dependable data pipeline, on the other hand, does not have to be complicated or time-consuming. LOGIQ simplifies the procedure and will help you get the most out of your data flow quicker than ever before.