Amazon Athena lets you query data where it lives without moving, loading, or migrating it. You can query the data from relational, non-relational…
5 Key Benefits Of Building A Centeralized Data-Lake
What are the key benefits of data lake for your business ?
Today’s organizations are unable to convert external and internal data sources into meaningful information.
They lack enough visibility into various key business processes,360-degree view of the customers & behavioral patterns, and hence unable to make informed and timely decisions leading to business risks and inefficient planning.
Following are the key benefits of building data lakes…
No More Data Silos
A data lake provides you the seamless access to all your data for more meaningful insights
Usually, data in most organizations is stored in various locations in different ways with no centralized access management. It’s challenging to have access to it and perform any kind of analysis.
Data-lakes break down these data silos and provide seamless access to the required data for meaningful insights and faster innovation.
A centralized data lake eliminates data silos i.e data duplication, multiple security policies, and difficulty with collaboration. The data is consolidated, cataloged, and offers downstream users a single place to look for all sources of data.
Store Your Data In Any Format
Build advanced analytics and predictive modeling capabilities
Data lakes eliminate any requirements of data modeling during the data-ingestion. You can store data in data-lakes in any format & medium i.e. RDBMS, NoSQL Databases, File Systems, and Time Series Databases, etc. Data can be loaded in its existing format like a log, CSV, XML, parquet, etc. without any transformation.
Data lakes are cheaper as compared to the traditional data warehouses as it allows you to store data without any pre-defined format or schema.
Since the data is stored in original or raw format, it is not contaminated. Therefore it’s always possible to fine-tune earlier analytics and develop new insights from the same historical data.
Data scientists can access the raw data when they need it using more advanced analytics tools or predictive modeling.
No Predefined Schemas
Maximize your organization’s data value and security
With data-lakes, there is no need to have a pre-defined schema. This helps to process the raw data without having any information on the type of analysis that might be required in the future.
Data lake empowers your organization with a cloud-based data intelligence capability that can maximize data value and security while minimizing your data liability.
It provides a low-cost scalable and secure storage solution with advanced analysis capabilities on a variety of data types.
Strong Foundation For ML & AI
Machine learning & AI-powered analytics
By having a centralized data repository in the form of data lakes, multiple data sets can be combined to train and deploy machine learning models to perform predictive analysis and data usage patterns.
Data in the data lake is stored in an open format, therefore It makes it easier for various ML/AI-based analytical services to process this data to generate meaningful insights.
Data lakes can process all data types with a very low latency including unstructured and semi-structured data like images, video, audio, and documents which are very critical for modern machine learning and AI-based use cases.
Handle Modern Use-Cases
Eliminate limitations of the traditional data warehouse and innovate more
Traditional Datawarehouse solutions are expensive, proprietary, and have many limitations to handle the modern use cases that most companies are looking to address.
The data lake concept was developed in response to these limitations of the traditional Datawarehouse solutions.
Advanced analytics and machine learning on unstructured data are the key priorities for organizations today. For this, the data lake offers the required massive scalability up to an exabyte scale.
Data lake uses a flat architecture and object storage to store data as compared to the old data warehouses which stores data in files or folders.
Data Lake Vs Data Warehouse
Organization requires both a data warehouse and a data lake as they serve different needs, and use cases.
Traditionally a data warehouse is an optimized database to analyze relational data coming from business applications. The data structure and schema of a data warehouse are already defined in advance to optimize it for faster queries.
A Data Lake is a large collection of raw data, which is not analyzed, and its actual objective is not yet defined.
In addition to the relational data from business applications, The Data Lake also stores non-relational data streaming from social media, mobile apps, and IoT devices. Data in any format can be stored at scale without any predefined schema or data model. Data Lake allows you to perform advanced analytics like big data analytics, full-text search, real-time analytics, and machine learning.
Why Should You Build Your Data Lake On AWS?
AWS offers purpose-built services for the best price-performance and scalability at the lowest cost
Choosing the right storage to support the data lake is essential to its success. Thousands of data lakes are hosted on Amazon S3. You can cost-effectively build and scale data lakes of any size in Amazon S3.
Amazon Simple Storage Service(S3) is designed for 11-9s of data durability.S3 empowers you to integrate AWS services such as Amazon Elasticsearch, Amazon EMR, Amazon Redshift, and Amazon Quicksight seamlessly run big analytics, artificial intelligence (AI), and machine learning (ML).
AWS serverless services such as Amazon Athena, Amazon Kinesis, and AWS Glue allow data manipulation and exploration without the need to deploy anything.
How Can We Help ?
We can help you build your modern data-lake solution with a centralized data repository with an integrated suite of analytical services. Know More...
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