Managing enterprise data pipelines is no small feat. Data engineers spend much of their time dealing with the sprawl of increasing data models, standardizing the development of data transformations, and evaluating impact analysis on changes within a project.
Luckily, the secret to building and maintaining pipelines sustainably and without burning hours on menial, repetitive tasks lies in what you’re constantly generating along the way: metadata. By purposefully using metadata in every aspect of the development lifecycle, you can build and manage pipelines more quickly, freeing up your time to focus on helping the business solve problems and generate value with data.
But how exactly does metadata come into play when building pipelines? Coalesce uses column- and table-level information to describe the structure and dependencies of your data projects. This speeds up the design and deployment of your data warehouse, while freeing you from having to manually define and build data objects. Designing with metadata helps your team to define your data warehouse with column-level understanding and introduces standardization across the entire data project.
Standardizing data objects
Coalesce uses the concept of nodes to develop data pipelines. Nodes are fully configurable building blocks that represent database objects, which are run and materialized within Snowflake. Every node uses a set of defined configuration and logic to consistently apply the same template or pattern. In this way, Coalesce customers can create their own nodes by defining the structure and behavior of particular transformation patterns through the DDL and DML that is run within Snowflake. Developing data pipelines by using different nodes removes the risk of inconsistent, inefficient data modeling. This gives you complete control over how objects are built within Snowflake, as well as the exact code that Snowflake runs to create and populate your objects.
Additionally, because nodes are standardized with metadata, Coalesce is able to reuse this metadata each time a node is added to a pipeline. This means that the development of any data pipeline will have the same standards applied for each node type.
For example, a common approach for transforming raw data tables is to use a staging layer for processing. Within Coalesce, you can apply a Stage node (which leverages standardized metadata) to every raw table within Snowflake. This means that every Stage node within your data pipeline is using the same template, providing consistent transparency across all of these nodes.
By standardizing development in this way, SQL users of any level can immediately understand how a node will be run and materialized, while also giving them the option to update these settings from a user interface, instead of manually coding configurations within messy YAML files.
Because all nodes are already defined and the metadata can be fully configured, development time is faster. Nodes can be consistently applied over and over again, without having to write any code. By automating the manual aspects of building pipelines, you can build consistently and focus on the business logic of your data projects.
Capturing dependencies automatically
Another advantage of building with a metadata framework is the automatic capture of all dependencies within a data pipeline. This way, any node used within a data pipeline will inherit all of the metadata from the node(s) it is dependent on — meaning that all of the dependencies within your data pipeline will be automatically generated.
This functionality supports the rapid development of data pipelines. Metadata is shared between nodes, allowing you to add nodes with just a few clicks or add multiple nodes to a pipeline at once. By generating dependencies automatically, you can focus on how to model the logic of a node, rather than writing and configuring dependencies each time a new node is created. This also means any dependent node(s) can immediately alert you to the impact of breaking changes happening anywhere in the pipeline.
New nodes in Coalesce automatically inherit the column metadata from preceding nodes. This means that each node is aware of metadata information, such as the data types and definitions of each column. This allows you to dramatically shorten development time by defining transformations once and then consistently applying them to columns in bulk, while defining multiple column metadata attributes at once. By taking advantage of metadata in this way, developers will always have the most accurate picture of the relationships and dependencies within their data project, as well as the flexibility to rapidly build data pipelines.
Achieving column-level control
As data projects grow, performing impact analysis becomes increasingly difficult. When considering any change to your data project, it is critical to understand how that change will impact the rest of your pipeline. While object-level lineage can provide a starting point, it’s nearly impossible to understand how the column-level changes within those objects will impact your pipeline.
With Coalesce, metadata is used to expose column-level lineage about any node, showing a complete picture of where a column is originating from and how any downstream nodes will be impacted by a change in that column. This means developers and business users can quickly and easily make actionable decisions about how to perform change management within a data project. Additionally, this mitigates the risk of breaking changes in data models when implementing changes, as data teams are able to view how any schema and column changes would impact any node within the pipeline.
Column lineage doesn’t end there. Another major advantage of Coalesce’s metadata architecture is the ability to immediately propagate column additions or deletions to upstream or downstream nodes. In thinking about legacy solutions, understanding how and where to propagate a column addition or deletion can be incredibly challenging and may result in breaking changes to your data models. Not to mention, the process of propagating column changes in these solutions requires manual coding, which is time-consuming and prone to error.
With Coalesce you can propagate columns with just a few clicks, which allows any user to mitigate the risk of missing data models where the column(s) should be handled and reduces the manual intervention needed to make the changes.
Creating a solid data foundation for future data projects
As the volume and complexity of data increases within organizations, it becomes more imperative that data teams use solutions that can sustainably transform, manage, and scale with their data. While code-first solutions provide developers with the freedom to develop data pipelines with little structure or constraints, this approach creates massive sprawl and unwieldy data projects.
By using a transformation platform that’s designed to use metadata from the ground up, developers can build faster while still harnessing the flexibility of code. Additionally, data teams can manage and scale their projects thanks to the multiple advantages that Coalesce provides within the developer experience through its metadata-centric architecture.
Especially in the age of increasing demands to support AI/ML capabilities, it’s more important than ever to have a data foundation that you can build easily, update quickly, and manage at any scale. By leveraging metadata throughout its entire platform, Coalesce allows data teams to proactively serve the business with data, rather than reactively managing an increasingly complicated data pipeline.
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