![]() Dremio – Dremio’s data lake engine provides analysts, data scientists, and data engineers with an integrated, self-service interface for data lakes.Databricks – Databricks, the Apache Spark-as-a-service platform, has pioneered the data lakehouse, giving users the options to leverage both structured and unstructured data and offers the low-cost storage features of a data lake.Amazon S3 – An object storage service for structured and unstructured data, S3 gives you the compute resources to build a data lake from scratch.Microsoft Azure – Microsoft’s cloud computing entrant in this list common among teams that leverage heavy Windows integrations.Firebolt – A SQL-based cloud data warehouse that claims its performance is up to 182 times faster than other options, as the warehouse handles data in a lighter way thanks to new techniques for compression and data parsing.Amazon Redshift – Amazon Redshift, one of the most widely used options, sits on top of Amazon Web Services (AWS) and easily integrates with other data tools in the space.Google BigQuery – Google’s cloud warehouse, BigQuery, provides a serverless architecture that allows for quick querying due to parallel processing, as well as separate storage and compare for scalable processing and memory.Snowflake – The original cloud data warehouse, Snowflake provides a flexible payment structure for data teams, as users pay separate fees for computing and storing data.Data storage and processingĭon’t forget the SQL query code (or whatever language you are using with Spark) is part of your data pipeline architecture! Data flows from table to table (or dataframe) within your data repository and query changes can dramatically impact quality and performance. ![]() Here are some of the most common solutions that are involved in modern data pipelines and the role they play. 5 Data pipeline architecture designs and their evolution ETL A well designed pipeline will meet use case requirements while being efficient from a maintenance and cost perspective. Why is data pipeline architecture important?įor data engineers, good data pipeline architecture is critical to solving the 5 v’s posed by big data: volume, velocity, veracity, variety, and value. For example, data may be extracted from multiple sources, reorganized, and joined at different times before ultimately being served to its final destination. It’s important to understand most data pipelines aren’t a linear movement of data from source A to target B, but rather consist of a series of highly complex and interdependent processes. This is commonly abbreviated and referred to as an ETL or ELT pipeline. This frequently involves, in some order, extraction (from a source system), transformation (where data is combined with other data and put into the desired format), and loading (into storage where it can be accessed). Let’s go! What is data pipeline architecture?ĭata pipeline architecture is the process of designing how data is surfaced from its source system to the consumption layer. Backcountry’s data pipeline architectureīe sure to read to the end where we share 6 data pipeline architecture diagrams used by real data teams at companies like JetBlue, Fox Networks, Drata, and more. ![]()
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