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Big Data are large datasets that contain useful information, such as customer data. These datasets are generated over the internet every millisecond, thus managing Big Data is nearly impossible using traditional systems, and companies need specialised systems. Companies can use services like AWS Big Data Analytics to get useful business insights from Big Data.
According to Statista, AWS leads the cloud market with a 32 per cent share. Its architecture is perfectly capable of managing storage-intensive Big Data. Thus, it’s the right time to invest in its services and leverage AWS’s benefits. But how can these services be helpful in managing huge datasets? Let's find out. In this blog, you will learn and understand what AWS Big Data is, its key features and capabilities and how it helps manage Big Data.
Table of Contents
1) What is AWS Big Data?
2) How do AWS Big Data solutions work?
3) Key features and capabilities of AWS in managing Big Data
4) Available AWS tools for Big Data
Big Data are large complex datasets which cannot be managed using conventional databases. These data are enormous in terms of volume, velocity and variety. So, it is tough for traditional Database Management Systems to handle these datasets. It needs specific Database Management Systems that are capable of managing such data.
AWS provides tools and services that can handle such vast chunks of data effortlessly. It helps organisations perform Data Management tasks like storage, data analysis and processing, etc. Its Cloud-based services help extract useful insights into your business using Big Data.
Numerous AWS Applications are employed for Big Data Management. This allows organisations to place complete trust in AWS services for their Big Data requirements without concerns about hardware, dependability, or security. AWS's seamlessly integrated services simplify the entire Big Data workflow, spanning from data extraction to end-user consumption. The following are the key reasons why AWS is chosen over other services:
a) Availability: AWS services remain accessible at every stage of the data flow, regardless of the data's scale.
b) Ingestion: Organisations demand rapid data retrieval from sources to storage. Various AWS services facilitate the extraction of data from sources in a matter of seconds.
c) Computing: AWS services harness powerful computing capabilities to execute tasks on Big Data efficiently.
d) Storage: Storing data securely, shielded from potential leaks or exposure, can be a challenging task for businesses. AWS storage services, such as Amazon S3, offer dependable and secure solutions for storing data while enabling data processing.
e) Security: Any security breach in the data pipeline can spell significant problems for businesses. AWS's integrable security services provide robust data security through the implementation of security policies and compliance measures.
Amazon Web Services provides numerous solutions that cater to managing Big Data. These cutting-edge tools and technologies enable organisations to gather data efficiently, securely store, and meticulously analyse their expansive datasets. It also does so in a highly cost-effective manner.
There are various AWS benefits of leveraging these solutions. The suite of tools and services available from AWS comprehensively supports the full life cycle of Big Data. It seamlessly guides data through every stage, from initial collection right through to its ultimate utilisation. Let's see how these solutions work during various stages of Big Data lifecycle:
Collection solutions are primarily geared towards facilitating the aggregation of both structured and unstructured raw data. In the AWS ecosystem, the process of collecting vast amounts of Big Data is bolstered by a robust set of services and capabilities. These include the following:
1) Kinesis Streams and Kinesis Firehose: These tools are instrumental for real-time data stream ingestion, making it possible to gather and process data in real time. It is critical for applications requiring immediate insights and responses.
2) Integration with diverse data sources: AWS Big Data collection solutions offer the flexibility to connect and integrate with services and data sources seamlessly. This can be achieved through manual import procedures or by utilising APIs, ensuring that data can be efficiently gathered and incorporated into your Data Management processes.
Storing vast volumes of Big Data demands exceptionally scalable solutions capable of managing data in both pre-and post-processing stages. In the AWS ecosystem, the storage of Big Data is fortified by a comprehensive set of services, which include the following:
1) S3 and Lake Formation: These services are instrumental for object storage, allowing you to store and manage your data securely.
2) S3 Glacier and Backup: These services are specialised for data backups and long-term archiving, ensuring that your data remains accessible and secure.
3) Glue and Lake Formation: These services are indispensable for cataloguing and organising your data efficiently, simplifying the process of data management.
4) Data exchange: This service facilitates the exchange of data with third-party sources, enabling seamless integration of external data into your storage and analytics workflows.
Processing and analysis solutions play a pivotal role in transforming raw data into formats that are readily consumable for analytical purposes. This transformation process encompasses activities such as data sorting, aggregation, schema adjustments, and data translation into various formats.
The Elasticsearch Service is well-suited for operational analytics, providing real-time data analysis capabilities for swift decision-making. Athena offers interactive analytics, empowering users to conduct ad-hoc queries and explore data thoroughly. For Data Warehousing needs, Redshift ensures efficient data storage and retrieval, facilitating in-depth analytics.
To manage large-scale data processing, EMR stands out as an ideal choice, enabling users to work with extensive datasets via popular processing frameworks like Hadoop and Spark. Kinesis Analytics specialises in real-time analytics, allowing the processing and analysis of streaming data as it happens, delivering timely insights for critical decision-making.
Consumption and visualisation solutions are instrumental in the extraction and communication of insights from your data. These tools facilitate the analysis of datasets and emphasise those aspects that are pertinent or offer precise predictions and recommendations.
Within the AWS environment, the consumption and visualisation of Big Data are enriched by services like Quicksight, which is designed for creating visualisations and interactive dashboards. Moreover, tools like Deep Learning AMIs and Sagemaker support Machine Learning and Predictive Analytics, enabling organisations to harness data-driven insights effectively.
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AWS Big Data Architecture is designed to facilitate seamless integration, scalability and security. From Data Warehousing to Data Analytics, it can effortlessly perform several functions. Some key features, capabilities and how they can help manage Big Data are explained below:
AWS offers one of its powerful services like Amazon Redshift, to help in effective Data Warehousing. It providesthe capability to examine massive datasets like Big Data. Apart from this, it also offers parallel processing and can perform multiple queries rapidly.
AWS offers specific services to help you connect with Big Data's workflows. Organisations can create, instruct and launch Machine Learning models and scale up the capacity. This enables them to deploy various services rapidly and helps fulfil the demands of your product.
It also offers ready-made Artificial Intelligence (AI) models that can perform various tasks like recognising images, predicting demand for your products and services and Natural Language Processing (NLP). Some of the services that can help you do this are listed below:
a) Amazon Comprehend: It is a Natural Language Processing service that can train AI models using Machine learning (ML).
b) Amazon Forecast: Like its name, it is used for forecasting useful business metrics analysis using ML.
c) Amazon Rekognition: It is used for image processing and video analysis using ML.
Organisations can perform enterprise-level Big Data analysis using AWS. They can also perform certain tasks like cataloguing data, cleansing data and data governance, and protecting data using encryption keys. Here are the services that help you complete those operations:
a) AWS Lake Formation: It helps ensure that data is available for various analysis by creating data lakes.
b) AWS Glue DataBrew: It is used for preparing visual data and helps clean data for performing data analysis.
c) AWS Key Management Service (KMS): It helps secure data and applications by letting you build and manage encryption keys.
Cloud providers like Amazon Web Services changed the game by introducing Serverless data processing. Here are the services that help organisations with serverless data processing:
a) AWS Lambda: Organisations can use it to run back-end services and applications without equipping a server.
b) AWS Glue: It helps people find, move and combine data from various sources using serverless data integration.
Raw data can sometimes be tedious and hard on the eyes. It is hard to quickly get insights from observing texts and numbers. Organisation need something that can help them visualise things to offer more perspective. AWS Glue and QuickSight do precisely that. It can help transform boring data into visually pleasing insights. With QuickSight, organisations can analyse and visualise data, and here's how Glue helps:
a) Cataloguing data: It helps prep the data for analysis by streamlining it on a centralised data catalogue.
b) Transforming data: It helps convert data into different formats using a data lake.
c) Loading data: It helps load massive data to tables using simple commands.
The challenge with using traditional data storage for businesses is constantly expanding it when they run out of storage. That leads to another challenge: a need for more physical space. Switching to a Cloud storage provider like AWS can provide virtually unlimited storage, and the best part is organisations don't need to worry about expanding physical space. Some of its storage options are listed below:
a) Amazon Elastic File System (EFS): It is a flexible storage system that can automatically contract and expand based on the requirement.
b) Amazon Elastic Block Store (EBS): It is a storage system designed explicitly for performance-intensive tasks.
c) Amazon Simple Storage Service (S3): It is a scalable storage system that you can use to store objects using a web interface.
Scalability is the key when it comes to expanding business. If you want your business to grow, it should be capable of releasing products and services on a large scale. Many businesses fail mainly because they fail to keep up with the rising demand. With AWS, they can maximise their capabilities of managing Big Data.
It is flexible when scaling up and down on the hardware resources to manage Big Data and significantly reduces cost. Apart from this, it helps rapidly launch products and services, further expanding the business.
Seamless integration is critical when choosing a provider for managing Big Data. The Big Data services of AWS help merge with its services very quickly. This allows businesses to use its serverless storage, messaging and computing features. Moreover, it also helps develop data pipelines from one end to the other.
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In Big Data, having the appropriate tools is essential for addressing challenges. Converting the immense raw data into actionable and valuable insights is daunting, but it becomes an achievable objective with the proper resources at your disposal.
Amazon Kinesis Firehose efficiently handles data compression, batching, encryption, and Lambda functions. It reliably transports real-time streaming data to Amazon's S3, ensuring seamless loading into data lakes, data stores, or analytics tools. Kinesis Firehose effortlessly adapts to the data processing demands of any organisation without the need for continuous administrative oversight.
AWS Snowball is a high-efficiency data transport solution that securely transfers large datasets from on-premises storage and Hadoop clusters into Amazon S3. Once you initiate a job via the AWS console, a Snowball device is automatically sent to your location.
Connect it to your network, install the Snowball client, and transfer your files and directories to the device. Once the transfer is complete, return the Snowball to Amazon Web Services, and they will seamlessly move your data into your designated S3 bucket.
Amazon S3 serves as a repository for data gathered from corporate applications, websites, mobile devices, as well as Internet of Things (IoT) devices and sensors. It boasts unparalleled availability and can accommodate virtually any volume of data. Amazon S3 leverages the same scalable storage infrastructure that powers Amazon's global eCommerce operations, underscoring its reliability and robust capabilities.
AWS Glue is a data service designed to streamline the Extract, Transform, Load (ETL) process by centralising metadata storage. With a few simple clicks in the AWS Management Console, Data Analysts can effortlessly create and execute ETL jobs. AWS Glue features an integrated data catalogue, serving as a durable metadata repository for all data assets. This enables Data Analysts to easily explore and query all their data from a unified perspective.
We hope that after reading this blog, you have understood everything about AWS Big Data and how it is managed. Apart from this, you would have also learned about its key features and capabilities. Managing and analysing Big Data helps extract useful insights to drive your business performance.
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