International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
Diving Into AWS Data Lake Sowjanya Vuddanti1, Sai Manvitha Reddy Mallireddy2, Naveen Kumar Reddy Renati3, Ramineni Udayasai4 1Sr. Assistant Professor, Artificial Intelligence and Data Science Department, LBRCE, Mylavaram, India 2Student, Artificial Intelligence and Data Science Department, LBRCE, Mylavaram, India
3Student, Artificial Intelligence and Data Science Department, LBRCE, Mylavaram, India 4Student, Dept. of ECE, DVR and DR.HS MIC college of technology, Kanchikacherla, India
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Abstract - Addressing the challenge of optimizing query
Amazon S3, Athena, and A Glue (ETL). Although Amazon S3 is a physical storage facility that can store data of any size, Amazon Athena is a query tool that makes it simple and effective to query Amazon S3 data. Data is prepped and processed using an extract, transform, and load (ETL) service called Amazon Glue before being loaded into a data lake. There are numerous advantages to constructing an AWS data lake. Above all, AWS enables scaling data processing and storage capacity simple and reasonably priced, which is crucial for managing massive amounts of data. Additionally, AWS offers a variety of services and resources for data analysis and storage, such as machine learning and artificial intelligence tools for deriving conclusions from data and formulating fact-based judgments. The affordability of establishing a data lake on AWS is one of its main advantages. With AWS, businesses only pay for the resources they use, so they can scale up or down in response to changes in their data processing and storage demands. When extra analysis and storage techniques are used, this might lead to considerable cost savings.
and storage efficiency within data management involves focusing on the conversion of CSV data into the Parquet format. This meticulous process aims to streamline data storage while significantly enhancing query performance. Through the seamless integration of AWS services such as Kinesis, Glue, Athena, and Lake Formation, a robust and efficient data ecosystem is established. Prioritizing automation enables the effortless creation and management of data lakes, ensuring scalability and adaptability in handling extensive datasets. Empowering organizations with real-time analytics, visualizations, and interactive dashboards powered by Quick Sight, the project facilitates informed, data-driven decisionmaking. Furthermore, the cost-effectiveness of AWS underscores the ability for organizations to pay only for the services they utilize. Embracing AWS Data Lake enables organizations to maintain a competitive edge, driving innovation, efficiency, and growth through the strategic utilization of data. This represents a significant advancement in data management methodologies, offering a transformative solution to maximize the efficiency and value of data resources in today's dynamic data-driven landscape.
In conclusion, AWS provides a robust and cost-effective platform for developing and maintaining data lakes. With a large choice of tools and services for managing and analyzing data, as well as flexible and scalable data storage and processing capabilities, AWS is a perfect platform for enterprises wishing to reap the benefits of data lakes. Businesses may get a competitive edge in today's datadriven business climate by visualizing data housed in a data lake.
Key Words: Query efficiency, data management, data ecosystem, Quick Sight, AWS Data Lake, competitive edge, data-driven decision-making, transformation in data management.
1.INTRODUCTION Data is the soul of modern enterprises, and the capacity to analyze and draw insights from data provides a significant competitive edge. Traditional data storage and analysis methods, on the other hand, can be sluggish and inefficient, making it difficult to handle and analyze massive amounts of data rapidly and efficiently. Many firms are turning to data lakes to meet this dilemma, a novel method of data storage and analysis that allows for more flexible and scalable data management. We investigate the usage of data lakes on Amazon Web Services (AWS), a cloud-based platform that offers a comprehensive collection of tools and services for developing and maintaining data lakes.
2.LITERATURE REVIEW In recent years, the proliferation of data warehouses and virtualization techniques has been instrumental in enabling organizations to leverage vast volumes of data for decisionmaking. However, despite their benefits, these traditional approaches come with their own set of limitations, particularly concerning scalability, performance, and adaptability to modern data formats. In this literature survey, we explore five key papers in the field and discuss their drawbacks in comparison to the emerging paradigm of data lakes, focusing on their challenges in handling CSV data and the potential for optimization in storage and query performance.
You can utilize a range of services provided by Amazon Web Services, or AWS, to construct your data lake, such as
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