Data analytics has been a rapidly changing area of technology, and cloud data warehouses have brought new options for businesses to analyze data. Organizations have typically used data warehouses to curate data for business analytics use cases. Data lakes emerged as another option that allows for more types of data to be stored and used. However, it’s important to set up your data lake the right way to avoid those lakes turning into oceans or swamps that don’t serve business needs.
Data Lake is a storage repository that can store a large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration. Data Lake is like a large container which is very similar to real lakes and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
The Data Lake democratizes data and is a cost-effective way to store all data of an organization for later processing. Research Analysts can focus on finding meaning patterns in data and not data itself. Unlike a hierarchical Data house where data is stored in Files and Folder, Data lake has a flat architecture. Every data element in a Data Lake is given a unique identifier and tagged with a set of metadata information.
The emergence of “keep everything” data lakes
Data warehouses require well-defined schemas for well-understood types of data, which is good for long-used data sources that don’t change or as a destination for refined data, but they can leave behind uningested data that doesn’t meet those schemas. As organizations move past traditional warehouses to address new or changing data formats or analytics requirements, data lakes are becoming the central repository for data before it is enriched, aggregated, filtered, etc. and loaded to data warehouses, data marts, or other destinations ultimately leveraged for analytics. Since it can be difficult to force data into a well-defined schema for storage, let alone querying, data lakes emerged as a way to complement data warehouses and enable previously untenable amounts of data to be stored for further analysis and insight extraction.
Data lakes capture every aspect of your business, application, and other software systems operations in data form, in a single repository. The premise of a data lake is that it’s a low-cost data store with access to various data types that allow businesses to unlock insights that could drive new revenue streams, or engage audiences that were previously out of reach. Companies store all structured and unstructured data for use someday; the majority of this data is unstructured, and independent research shows that ~1% of unstructured data is used for analytics.
Once the data lake is migrated and new data is streaming to the cloud, you can turn your attention to analyzing the data using the most appropriate processing engine for the given use case. For use cases where data needs to be queryable, data can be stored in a well-defined schema as soon as it’s ingested. As an example, data ingested in Avro format and persisted in Cloud Storage enables you to:
Reuse your on-premises Hadoop applications on Dataproc to query data
Leverage BigQuery as a query engine to query data directly from Cloud Storage
Use Dataproc, Dataflow, or other processing engines to pre-process and load the data into BigQuery
Use Looker to create rich BI dashboards
Connections to many common endpoints, including Google Cloud Storage, BigQuery, and Pub/Sub are available as fully managed connectors included with Confluent Cloud.
Here’s an example of what this architecture looks like on Google Cloud: