Data Lake is known to feature all the capabilities that are needed to make it easy for the developers and data scientists to store and retain their data.?
Not only that but data lakes help the analysts to store data of all sizes, shapes, and process it by all types and analytics across different platforms and languages.
Data Lakes remove the complexities of storing your data while making it really fast to get up and running with interactive analysis and streaming.
The Azure Data Lake also works with different kinds of IT investments that are already existing. It helps them in identifying, managing, and securing the simplified data management and governance.
It also integrates with different kinds of operational stores and data warehouses for extending current data applications.
The Azure Data Lake also solves different kinds of productivity and scalability challenges which prevent you from maximizing the value of your data assets that are already perfect to meet your future business needs.
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SQL on Data Lakes
By far, it has already been said that the performance issues and scalability usually have very little to do with SQL and more to do with the design of the databases itself.
One of the biggest advantages of SQL is that it can provide the expressiveness to analyze the data and provide familiarity as well. This robustness comes from the foundations of Set Theory.
And with Data Lake, the technologies we see are:
- Out of the different SQL layers, Prestor is the query layer and finds very wide usage in Amazon Athena, Qubole, and Google Cloud DataProc.
- The favored data catalog is the hive metastore.
- Out of the metastores, Spark and Spark SQL are widely used as well.
- Hadoop file system isn’t widely used and the cloud storages are fairly more popular than others with the CSV, Avro, and Parquet file formats.
Extract Load Transform (ELT) and SQL?
The ELT paradigm of data processing always puts the data transformation step at the end. This extraction from the source systems and loads into the database.
On the other hand, the old ETL of RBAR (Row-By-Agonizing-Row) is in direct contrast with the set-based processing that’s performed by Relational databases and set-based processing that formed the whole basis of SQL.
When it comes to ELT, we extract the data from the source databases and put the same in the data lakes. The SQL transformations are done in the Cloud Data Warehouses and the whole data is loaded to the target tables.
The ELT toolchain also uses Directed Acyclic Graph tools that include Apache AirFlow instead of the AutoSys-like schedulers of the old ETL toolchain.
AWS or other tools feed into the Data lakes or other warehouses and the minimal transformation takes happens between source systems and the loading areas. The same data is transformed into warehouses and analytics using SQL.
Despite a lot of challenges faced by data lakes, it’s safe to figure out that 80% of the data is unstructured and as more businesses turn to Big data in the future, data lakes are going to become beneficial for more users.
Unstructured data withholds information that can’t be kept in data warehouses. Even though they are strong in structure and security, big data just needs to flow freely into data lakes for better access to users without storing their information at all.
Layers of Data Lakes
Raw Data Layer – Also called landing area, raw events are stored for historic references.?
Cleansed Data Layer – Also known as the conformed layer, raw events are mastered into consumable data sets. The aim is to segregate the files in terms of formatting, encoding, data types, and content.
Application Data Layer – Known as a workspace or governed layer, this layer helps in applying the business logic to the cleansed data for producing data that’s readily available to be consumed by applications.
Sandbox data layer – It is the optional layer that’s used to play in. It is also called the exploration layer or data science workspace.