With today’s data’s volume, velocity, and variety, experts acknowledge that there is no one-size-fits-all database.
Traditionally, the viable solution was to build a data warehouse: extract data from different sources, clean and combine it, and then load it into well-defined data warehouse tables. The most effective way is to integrate a data lake with a data warehouse. Let’s find out why.
Creating a staging area for your data warehouse
Data is constantly in motion, changing forms and shapes. Modern data platforms should enable easy data ingestion and discovery while also providing a thorough and rigorous structure for reporting requirements. The typical emerging pattern is that a data lake serves as an immutable layer for your data ingestion. From which nothing is deleted.
Data lakes contain all raw data ingested into your platform. Using Kimball, Inmon, or Data Vault methodologies, including Slowly Changing Dimension Historisation and schema alignment, you can still have ETL (extract, load, transform) jobs that transform and clean the data and then ingest it into your data warehouse.
Data lakes and data warehouses don’t have to be mutually exclusive. It is possible to have a data lake as an immutable staging area and a data warehouse for BI and reporting.
Using a data lakehouse, Databricks combines both worlds into one solution. In the same way, platforms such as Snowflake enable you to use cloud storage buckets like S3 as external stages, effectively leveraging the data lake as a staging area. You need to decide whether a single data lakehouse or a combination of a data lake and a data warehouse makes the most sense for your situation.
To satisfy regulatory requirements, it is often necessary to maintain an audit trail. It is easy to collect metadata about when and which users ingested the data in data lakes. Not only can this be helpful for compliance purposes, but also for tracking data ownership.
Intensify the value-to-insights cycle
In providing an immutable layer of all data ever ingested, data is immediately available to all consumers. With raw data, you enable exploratory analysis that would be difficult to accomplish when different data teams use the same dataset in other ways. Data consumers may require various transformations based on the same raw data. Using a data lake allows you to dive anywhere into a wide variety of data and decide what might be helpful to you.
Real-time and batch analytics on a single platform
A data warehouse still has a difficult time integrating real-time data. Even though there are tools in the market that aim to solve this problem, it is much easier to resolve it by using a data lake as an immutable layer to store all of your data. Many solutions, such as Amazon Kinesis Data Streams or Apache Kafka, allow the S3 locations to be specified as sinks for data.
Cost-efficient
Over time, it is becoming increasingly expensive to store all the data you collect from social media, sensors, logs, and web analytics in a data warehouse. It isn’t easy to scale traditional data warehouses because storage and processing are tightly coupled. A data lake scales storage and processing (queries and API requests to retrieve data) independently.
Accessibility
Data warehouse solutions require the management of underlying compute clusters. Many cloud vendors realised the pain of building either fully managed or serverless analytical data stores.
For example, when you use S3 with AWS Glue and Athena, your platform is fully serverless, and you don’t pay for anything you don’t use. Using this platform, you can retrieve relational and non-relational data, query historical and real-time data, checkpoint your ML training jobs and serve ML models, and combine your data from the data lake and data warehouse via external tables.
Adaptable to changing circumstances
Almost one-third of the data in a data warehouse is never used. These data sources are ingested, cleaned, and maintained. This means that data engineers spend a lot of time and energy building and maintaining something that may not even yet have a clear business need.
The ETL paradigm allows you to save engineering time by building pipelines only for the use cases needed while storing all the data in a data lake for future use cases. In the future, if you have a business question, you can find the answer because the data is already available. However, you don’t have to clean and maintain data pipelines for something without a clear business use case.
Another reason why data lakes and cloud data platforms are future-proof is that as your business grows, the platform can support it. To accommodate their growth, you don’t need expensive migration scenarios. Regardless of the cloud data platform chosen, you should be able to grow your data assets virtually limitless.
Conclusion
Data lakes and data warehouse solutions with data lake capabilities are vital elements of any future-proof data platform. Additionally, having an immutable data ingestion layer that stores all data ever ingested are highly beneficial for audits, data discovery, reproducibility, and fixing mistakes in data pipelines.
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