Building a Better Data Ecosystem with AWS Data Lakes

Overview

As more gadgets connect to the internet, the amount of data increases. IoT is producing so much data that traditional systems cannot keep up with it. Data engineering focuses on designing the data pipeline to manage the data, and it is always changing to handle the data’s volume and velocity.

What is Data Lake?

An infrastructure that expands to meet changing organizational needs is known as a data lake. It offers storage for all types of data, numerous processing modes, and different kinds of analytics.

Raw data and processed data are the two data forms typically stored in Data Lake. Data can be traced using raw data at every stage of its lifecycle, including ingest, storage, processing, analytics, and application. A data lake supports a variety of computational engines, including batch and stream processing, interactive analytics, and machine learning, which can be used to analyze data and derive new insights from it. Additionally, because a data lake contains data in various formats and data types, it enables the multi-modal storage engine.

The big data processing echo system has evolved from Hadoop map-reduce to batch processing and stream processing. The computation paradigm has changed at each stage to keep up with the big data’s volume and pace. Map-reduce was slow since it saved intermediate steps to the disc. Apache Spark solved that issue with in-memory data processing, although it was made for bounded datasets. Low latency processing is intended for the unbounded data stream from Apache Flink. Data Lake uses these processing engines to process the unprocessed data.

Data lakes offer data management, access, migration, and governance features in addition to storage and processing. The overall lifecycle or several phases in a data lake are depicted in the diagram below. Typically, it will retain the raw data and most likely have a retention schedule based on the requirements of the enterprise. After processing the data, analytics are produced and stored for end-user applications.

Why do we need Data Lakes?

Data warehouses (DWH) have historically been used to store and extract insights. However, the DWH is overwhelmed by data’s rising volume and velocity. DWH stores data from business applications in an organized format instead of a data lake, which stores data from any source, such as an IoT device, logs, mobile gameplay, and online user activity.

DWH is schema-on-write, which means that a schema must be built based on business requirements before dumping data into the data warehouse. Since it was created with the type of queries in mind and the underlying engine doesn’t need to combine numerous tables to get the results, getting business intelligence is simpler and quicker. The data lake, however, has schema-on-read and is flexible enough to adapt to unforeseen business changes and data types. Although it allows for flexible data storage, effective data consumption requires data processing methods.

Compared to a data lake, which uses bulk storage, a data warehouse is more expensive since it requires premium storage to offer good performance. Additionally, DWH demands significant administrative expenses for backup and upkeep.

DWH restricts everyone’s investigation by only allowing access to those with certain skills, requiring access through the compute layer, and requiring access through the DBA. Data lakes, on the other hand, give users access to both storage and computation. Additionally, it gives users access to unprocessed datasets for further exploration and research.

The data lake can be scaled more easily than the DWH since cloud storage is less expensive. So, we don’t have to worry about paying much money to store things.

How to design Data Lake Solution (AWS)?

An enterprise data lake can be created using the management component offered by AWS called AWS Lake Formation. The data input, data accumulation, and data application are depicted in the diagram below.

Different data sources, including S3, NoSQL, and AWS relational databases, are supported by data lake systems. Data is gathered from various databases and object storage and moved into the lake storage, S3, with lake construction. Additionally, you can develop a data catalog and process your data using additional methods, such as machine learning. Later, different analytics tools like Athena and Redshift can use these datasets to provide insights.

Conclusion

The practice of storing all types of data in a single storage system, or “data lake,” which is analogous to a lake into which water is streamed from various sources, has changed as technology has advanced. Data is also sent into the Data Lake from many platforms and formats. Data lake architecture optimizes the storage and computation to provide query responses from the data more quickly, regardless of the storage or format.

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