1623236940
Data analytics on AWS: The Complete Guide for A Great Start
With human society transforming to a wholly digitally connected community, the amount of generated and collected data is growing outstandingly. Nowadays, we have enormous amounts of data available about both internal business processes and customer’s behaviors.
This provides us the opportunity to exploit ever-larger quantities of information of ever-better quality at a reduced cost.
A fitting example is customer behavior profiling: businesses can gather information about what products customers use, how they use them, which aspects of each product are really relevant in their daily life, and a lot more.
Moving from everyday life to industry, it is now possible to obtain information on which components of machinery are subject to wear and act accordingly (predictive maintenance). It is also possible to obtain data about defective pieces to improve the production lines, and so on.
The ability to understand which data to extract, collect it efficiently, store it at low cost, and finally to analyze it is therefore what really makes the difference, and it is a significant competitive advantage.
The effort to analyze this astonishing amount of data becomes challenging using only traditional on-premise solutions. AWS provides a broad set of fully managed services to help you build and scale big data applications in the Cloud. Whether your application requires real-time streaming or batch processing, Amazon Web Services has the services needed to build a complete data analytics solution.
The data analysis pipeline
To collect, store, and analyze data we need to deep dive into 4 fields that are commonly needed regardless of the type of project and implementation: Collection, Data Lake / Storage, Processing, Analysis and Visualization.
So generally, the steps of a typical data analysis pipeline can be summarized as follows:
- An appropriate infrastructure collects (ingests) the data from the field.
- The data is stored using an appropriate storage service, optimizing the data access pattern.
- Data is then processed by reading the input from the storage service, performing the required operations, and then storing the processed data in another location.
- Eventually, the processed information can be visualized using a business intelligence (BI) tool to obtain the dashboard and data presentation for the end-users and the business.
In the following sections, we will discuss each of the steps, with a focus on AWS services you can leverage to seamlessly implement the pipeline steps.
Collection
In order to design the correct data collection system, you need to think about the features of the data and consider the expectations regarding data latency, cost, and durability.
The first aspect to consider is the ingestion frequency, which It is the measure of how often the data will be sent to your collection system. It can also be referred to as the temperature (Hot, Warm, Cold) of the data. The frequency of the input data dictates the kind of infrastructure to design. Transactional data (SQL) are better ingested using tools like AWS Database Migration Service, while real-time and near real-time data streams are the perfect use case for Amazon Kinesis Data Streams and Kinesis Firehose.
Amazon Kinesis Data Streams is a reliable, durable, cost-effective way to collect large amounts of data from the field and from Mobile or web applications. One of the many advantages of Kinesis Data Streams is that you can extend its functionality with custom software to meet your needs exactly. It may store the data it has collected for up to 7 days, and it supports multiple applications producing data for the same stream. You can provide custom software leveraging Lambda Functions.
Amazon Kinesis Data Firehose fully manages many of the manual processes that Kinesis Data Streams requires, and also includes no-code configuration options to automatically deliver the data to other AWS services. It makes it easy to group the data into batches, and make aggregations. Kinesis Data Firehose streams can be configured with consumers, including Kinesis Data Streams, Amazon Amazon S3, Amazon Redshift, and Amazon ElasticSearch.
Cold data, which is generated by applications that can be periodically batch-processed may be efficiently collected using Amazon EMR or AWS Glue.
#data-visualization #data-analysis #data-processing #aws #data-lake
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1620629020
Getting Started With Data Lakes
Frameworks for Efficient Enterprise Analytics
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
Introduction
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
1620466520
Your Data Architecture: Simple Best Practices for Your Data Strategy
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
1624399200
Top 10 Big Data Tools for Data Management and Analytics
Introduction to Big Data
What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.
To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.
List of Big Data Tools & Frameworks
The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:
- Big Data Framework
- Data Storage Tools
- Data Visualization Tools
- Big Data Processing Tools
- Data Preprocessing Tools
- Data Wrangling Tools
- Big Data Testing Tools
- Data Governance Tools
- Security Management Tools
- Real-Time Data Streaming Tools
#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics
1623236940
Data analytics on AWS: The Complete Guide for A Great Start
With human society transforming to a wholly digitally connected community, the amount of generated and collected data is growing outstandingly. Nowadays, we have enormous amounts of data available about both internal business processes and customer’s behaviors.
This provides us the opportunity to exploit ever-larger quantities of information of ever-better quality at a reduced cost.
A fitting example is customer behavior profiling: businesses can gather information about what products customers use, how they use them, which aspects of each product are really relevant in their daily life, and a lot more.
Moving from everyday life to industry, it is now possible to obtain information on which components of machinery are subject to wear and act accordingly (predictive maintenance). It is also possible to obtain data about defective pieces to improve the production lines, and so on.
The ability to understand which data to extract, collect it efficiently, store it at low cost, and finally to analyze it is therefore what really makes the difference, and it is a significant competitive advantage.
The effort to analyze this astonishing amount of data becomes challenging using only traditional on-premise solutions. AWS provides a broad set of fully managed services to help you build and scale big data applications in the Cloud. Whether your application requires real-time streaming or batch processing, Amazon Web Services has the services needed to build a complete data analytics solution.
The data analysis pipeline
To collect, store, and analyze data we need to deep dive into 4 fields that are commonly needed regardless of the type of project and implementation: Collection, Data Lake / Storage, Processing, Analysis and Visualization.
So generally, the steps of a typical data analysis pipeline can be summarized as follows:
- An appropriate infrastructure collects (ingests) the data from the field.
- The data is stored using an appropriate storage service, optimizing the data access pattern.
- Data is then processed by reading the input from the storage service, performing the required operations, and then storing the processed data in another location.
- Eventually, the processed information can be visualized using a business intelligence (BI) tool to obtain the dashboard and data presentation for the end-users and the business.
In the following sections, we will discuss each of the steps, with a focus on AWS services you can leverage to seamlessly implement the pipeline steps.
Collection
In order to design the correct data collection system, you need to think about the features of the data and consider the expectations regarding data latency, cost, and durability.
The first aspect to consider is the ingestion frequency, which It is the measure of how often the data will be sent to your collection system. It can also be referred to as the temperature (Hot, Warm, Cold) of the data. The frequency of the input data dictates the kind of infrastructure to design. Transactional data (SQL) are better ingested using tools like AWS Database Migration Service, while real-time and near real-time data streams are the perfect use case for Amazon Kinesis Data Streams and Kinesis Firehose.
Amazon Kinesis Data Streams is a reliable, durable, cost-effective way to collect large amounts of data from the field and from Mobile or web applications. One of the many advantages of Kinesis Data Streams is that you can extend its functionality with custom software to meet your needs exactly. It may store the data it has collected for up to 7 days, and it supports multiple applications producing data for the same stream. You can provide custom software leveraging Lambda Functions.
Amazon Kinesis Data Firehose fully manages many of the manual processes that Kinesis Data Streams requires, and also includes no-code configuration options to automatically deliver the data to other AWS services. It makes it easy to group the data into batches, and make aggregations. Kinesis Data Firehose streams can be configured with consumers, including Kinesis Data Streams, Amazon Amazon S3, Amazon Redshift, and Amazon ElasticSearch.
Cold data, which is generated by applications that can be periodically batch-processed may be efficiently collected using Amazon EMR or AWS Glue.
#data-visualization #data-analysis #data-processing #aws #data-lake
1617969300
Alteryx Provides Free Access to Its Data Science Courses for Recent Graduates
The overnight transformation of companies adopting new technologies and transitioning to a digital work environment amid pandemic has made upskilling the most critical component in a worker’s repertoire in 2021. While information, data and the ability to make the right decisions serve as a stabiliser across verticals, analytics and data science have become indispensable tools to navigate today’s career scene.
According to a recent Forrester study, the top two challenges decision-makers cited are — the lack of employees with data skillsets and the lack of skills among business users who must use data insights. Almost 66% of organisations believe there is a requirement for data literacy among employees, where 59% demand analytic efficiency. However, with a converged approach to analytics through democratising access to data, automating tedious and complex processes, and promoting upskilling of data and knowledge workers, organisations can create a thriving data and analytics culture within.
#featured #advancing data and analytics #alteryx adapt #alteryx advancing data and analytics #alteryx upskilling programs #analytics upskilling #data and analytics #data science and analytics #start your analytics journey with adapt