Big Data has recently been called the new oil in the sense of business and government structures depending on it. This means that the cost of this product should approximately correspond. But there is one crucial difference: oil is an exhaustible resource; it becomes less, and there is more data every day and in progress. That is, the role of AWS data engineering is growing inexorably, current specialists are getting more work, and new people are needed who can pump this "oil" and transport it, including using cloud services such as AWS.
Over the past ten years, Internet users have more than doubled. Each of them generates data and uses the results of their analysis.
What is hidden behind the letters ETL?
Things are like this: there is a large amount of data and a platform for processing it on the one hand; there is a Big Data scientist who applies mathematical data models according to the needs of the task set by the business; and there is an AWS data engineering (as in DATAFOREST) who has to "make friends" the theoretical ideas of a scientist with the practical capabilities of the platform using frameworks and cloud services, in our case — AWS.
The technical tasks of AWS cloud platform data engineering can be divided into groups:
- Creating such a structure of data processing to get an effective ETL — extracting, transforming, loading; this is the so-called pipeline data
- Development of a flexible data storage and access mechanism by selecting the correct type of database and debugging processes
- Data processing by structuring, typing, and cleaning with the search for failures and anomalies in algorithms for their further elimination
- Calculation of the infrastructure and resource capacities, which will make it possible to implement the construction of these systems practically
In working with Big Data, AWS data engineering provides the foundation of the hierarchy by collecting, processing, and transforming data.
Who uses the results of data engineering?
In the early 2010s, social networks, streaming platforms, and real estate services acquired so many visitors that a new IT specialty, a data engineer, was required to process their data. It was necessary to create tools, platforms, and infrastructure for fast, reliable, and high-quality information collection and its further processing as material for future analysis.
Customers' behavior, demands, and interests had to be explored within the framework of the Value Proposition Canvas concept — either to find a place in the market for a new business or to strengthen the position of an existing one.
As a result, entrepreneurs have benefited from AWS data engineering in the following ways:
- Reliable and correct data of high quality in an accessible form for analysis
- Competitive advantages in the form of a unique value proposition
- Possibility of scaling following the dynamics of the received data
End users have also felt the benefits of AWS data engineering, as businesses have begun to understand their needs without tedious surveys and ubiquitous registrations. If we recall the metaphor that business people and customers walk on opposite sides of the street, working with AWS data engineering has made this street entirely pedestrian.
What does Amazon Web Services (AWS) offer?
Amazon Web Services is a cloud-based platform (such as Google cloud) that works on a pay-when-you-use basis. With AWS cloud services, you can find the right data tools and principles for working with AWS data engineering without the cost of acquiring them. It is a supermarket where you can taste food and pay for a bitten-off piece. If you like, you can buy it in full and eat it at home.
This approach can significantly reduce research costs, which is very convenient for organizations with limited budgets — start-ups and government agencies. However, the flexibility of AWS data engineering and rapid innovation benefits all companies without exception.
As for the categories of AWS data engineering services, they can be divided as follows:
- Analytical tools and data lakes — storage and sorting in one repository
- Services to support machine learning environments
- Serverless Computing — run applications in the cloud with the exact number of resources allocated
- Storage of large amounts of data — cost-effective solutions for backups (such as AWS Storage Gateway), disaster recovery, and archiving
Many AWS data engineering products and services have free trials — bite-sized pieces of bite-sized pieces.
Getting AWS data engineering tools ready
Amazon Web Services is an end-to-end platform that is a service in itself, just like the proposed infrastructure. There are many tools to choose from, and the first task is to make a choice and customize the workspace for yourself for greater efficiency. You must first deal with local and small data to work with global Big Data.
Log in to the AWS data engineering virtual workshop
To get started with Amazon Web Services, you need to create an account, and there is a free registration option, but with some restrictions. All it takes is three simple things: a credit card, a phone, and an email. The sequence of actions is essentially not much different from the usual registration on other resources:
- Acquaintance with the documentation
- Create an account
- Choosing an Infrastructure Service
- Phone verification
- Create a server resource
- Connecting to a server resource
The last procedure may take several minutes.
AWS data engineering access control
Your account is a virtual office where people work, and their intellectual property and production equipment are located. Of course, you would not want outsiders to enter the office and interfere with work. To do this, you must set up a "pass-through system" — AWS Identity and Access Management (IAM). With its help, you can:
- Set and manage access and load restrictions
- Manage the identity of a single account or group of AWS data engineering accounts
- Grant security permissions to AWS resource loads
- Analyze access rights for the least privileges to software modules
AWS IAM specifies access to services and resources, manages local restrictions, and refines permissions. These are integral parts of AWS data engineering.
AWS Configuration Item
Let's continue the analogy with the office and remember that there are rules of conduct there — do not litter, hold meetings in a particular room, set the alarm when you leave, etc. A set of such practices on the AWS data engineering platform is called a configuration, which is recorded in the AWS Config configuration item. These rules are of two types:
- AWS rules — pre-configured and controlled by the AWS data engineering platform itself, and the user selects the desired power, activates and enters configuration parameters
- User rules — AWS Lambda platform service creates a function for the account, and it becomes part of the user rules
A rule of 150 can be created for a single account by default, but this number can be increased upon request on the AWS Service Limits page.
An AWS data engineering pipeline for "new oil"
A data pipeline entirely justifies its name. The data is "pumped" from multiple sources, converted, and sent to the storage for subsequent analysis. Building an effective data pipeline is the main task of AWS data engineering. After ingesting and processing, the data fall into the centralized base (storage) or unstructured storage (data lake).
The first section of the AWS data engineering pipe: Data Ingestion
It is the process of extracting and sending the data just received. They can be transmitted in two ways: in real-time streaming and in portions (packages and micro packages). A sign of effectiveness: the correct selection of priorities by data from various sources.
Problems related to entering an extensive array of data:
- Scale: the larger amount of data leads to the lower quality
- Safety: data can be in two points at the same time, which increases their external availability
- Separation and association: various flows can duplicate each other's functions, and many data from different sources may not get into one AWS data engineering pipeline
- Maintenance of quality: keeping a constant level and quality of data is not easy, although quality testing is obliged to enter the data input system
- High cost: an increase in the amount of data leads to storage and processing costs, including using AWS services
It would help if you did not confuse data input with ETL. The first includes the whole range of ways to extract data with saving quality, and the second is preparation for transferring data to the storage from one or several sources.
The AWS data engineering choice of sources
Each data source can be filtered so that only the necessary data from the table can be. Filter parameters can be approximately the following:
- Selecting characteristics for the table
- The entire source or objects inside the data frame is used
- Request on the layer of data
- Filtering on attributes, location, uniqueness, number of returned objects
Thus, the probability of obtaining only the necessary quality AWS data engineering increases.
Data pumping options from AWS
For the needs of AWS data engineering, the platform has several popular tools based on different principles.
For example, a serverless AWS Glue data integration service operates according to the ETL concept, simplifying the search, preparation, and loading of data from many sources for subsequent analysis, machine learning, and application development.
AWS Data Pipeline is a cloud service planning to move and process cloud data. It acts based on a conveyor performing routine operations and exempts the time and effort to analyze the data or other types of AWS data engineering.
The second AWS data engineering section: oil storage
A specific volume of oil is stored at a set temperature, pressure, and humidity. The data obtained should be stored, considering archiving, cataloging, and security. It applies to the field of data storage management. Since the data is more expensive than oil, it must be stored more carefully. Before the AWS data engineering storage election, make sure that:
- the storage method corresponds to the request
- the storage occurs in the cloud
- data management principles take into account the characteristics of the industry
Also, decide on the choice of AWS data engineering access, work, and data transfer:
- Direct connection to the user's computer using a cable
- A few computers on the net with the same access rights, but they can be divided into levels
- Network storage using fiber-optic communication, which allows you to transmit large volumes of information
You can choose any AWS data engineering method, but from the proposed one, only cloud network storage has sufficient mobility and throughput opportunities.
Data storage from the AWS platform
Amazon Simple Storage Service (Amazon S3) is a service for storing objects that gives AWS data engineering high performance, scalability, accessibility, and data security. Lakes of data, cloud, and mobile applications are equipped with flexible administration tools, which allow you to reduce costs, structure, and protect data.
In the process of AWS data engineering, the data is often required for lakes — a place for a structured and unstructured volume of data coming from many sources. AWS Lake Formation creates such lakes in a convenient way to analyze. The advantages are approximately the same:
- In a short time, the creation
- Scalable protection and management
- Independent analytics
- Building a network with minimal data moving
The service allows you to standardize access to many AWS data engineering end users.
In the first months of 2019, Amazon Web Services accounted for 13% of Amazon's net sales and 62% of the company's operating income
The third AWS data engineering section: oil refining
Crude oil is not suitable for consumption, it needs to be processed, and different types of fuel and raw materials for further processing are obtained: gasoline, kerosene, diesel, oil, and tar. It is the same with AWS data — at the source it has one form, but they need to be delivered to the endpoint in a different format.
And this is where the analogy of AWS data with fuel is interrupted since oil products are first made from oil and delivered to customers, and the data can be immediately uploaded to the client's AWS storage, and the necessary fractions can be "caught" from there.
It's all about the emergence, rapid development, and popularization of cloud storage. The ETL method mentioned above has turned into ELT because of this. Swapping one letter means a lot more than it seems. Since the AWS transformation and loading have changed places, the essence of the method has changed.
The attitude towards the first stage of AWS data extraction has also changed. It is implemented in three ways:
- Total extraction — new and revised AWS data do not differ; everything is downloaded
- Partial extraction with AWS data update notifications
- Partial extraction without AWS data update notifications
When using ETL, you need to immediately know what AWS data to load, and in the case of ELT, everything is retrieved in a row, and the user himself will figure out what suits him.
When AWS data is transformed in the second stage, filtering operations are laid down in the preparatory stage, and AWS data engineers do this. In this case, the transformation is performed once, and if a new analysis methodology is needed, a new AWS data pipeline may be required. Suppose the data is loaded into the lake at the second stage. In that case, the transformation can take place as often as you like, and AWS data analytics can help data engineers build a methodology and direction for modifications.
During the load phase in the AWS ETL variant, the data is moved from the staging database to the target storage. It is done by physically inserting new rows into the storage table using SQL commands or a batch load script. ELT skips the intermediate phase and loads the raw data directly into the target AWS storage, saving much time from fetch to delivery.
For most key usage parameters (flexibility, cost, maintenance, download, and conversion time), AWS ELT wins. ETL has better compliance and tools and a more extensive selection of practitioners. The final choice always depends on the needs of the company.
How to process data in AWS
For convenience and to improve the quality of AWS data processing and to take into account specific tasks, there is a particular set of options from the AWS data engineering set:
- Amazon SageMaker
- Apache Spark
- Self-Managed Stacks
- Other AWS services with little or no code
For example, Amazon Elastic MapReduce (EMR), a cloud-based AWS environment for processing, analyzing, and machine learning, runs Apache Spark — the open-source parallel processing framework — for unstructured and semi-structured data. It supports in-memory processing to improve the efficiency of applications that analyze large and complex AWS data.
The above options can be used in combination, not only separately. For example, some teams prefer SQL, while others use Spark for specific tasks in addition to Python frameworks. It should also be remembered that not all AWS services have the option of data visualization.
Integration for AWS transformation and analysis
You can use the combination of AWS data engineering services for complex solutions for AWS data transformation and analysis. The perfect combination of AWS data transformation and reaching the analytical stage is the integration of the efforts of AWS Amazon Athena and AWS Glue.
AWS Athena is an interactive data analytics service that facilitates AWS Simple Storage Service (S3) analysis using Python or SQL tools. It's a serverless AWS service; there's no infrastructure to set up or manage, so you can immediately start analyzing.
AWS Athena for SQL uses the AWS Glue managed directory to store databases and tables stored in AWS S3. It configures Athena to use directories. Athena uses its own directory in regions where AWS Glue is unavailable.
A single AWS data catalog provides a single repository for metadata and automatic recognition of AWS data sections and schemas.
The fourth AWS data engineering section: how to show?
The last section of our imaginary pipe in which oil was pumped, and AWS data was obtained is the visualization of data for human analysis with high user experience. To understand the essence of complex processes occurring at the data level, an AWS data engineer needs to display information in a form that is understandable to the human mind: graphs, charts, and animations. And in this form, the AWS data is ready for analysis.
The goal is to create an object that is as easy to understand as possible and takes little time to recognize.
Proper AWS visualization is suitable for use in education (conducting online training), in a scientific environment (artificial intelligence), and for simply transferring information from one businessman to another.
AWS data engineering has its solutions in providing data in a "human" form. For example, AWS Data Exchange searches, fetches, and subscribes to data from any industry. The AWS Data Exchange directory links to data sources for analytics and machine learning experiences. When you connect data from AWS Data Exchange to Amazon QuickSight, critical information becomes human-readable.
Amazon QuickSight is a business intelligence service that allows information to be distributed to every AWS user. The service creates and publishes interactive dashboards of machine learning data. It can be accessed from any device and embedded in an app, portal, or website.
Analyze this with AWS
The platform provides various solutions for efficient results analysis as part of the AWS projects' data engineering tools. Modern AWS cloud capabilities have turned storage into complex structures capable of processing Big Data in real-time and in batches, including if they come in an unstructured form. AWS' data engineering tools provide a wide range of opportunities while maintaining high scalability and security. There are many of them, and their functions are very different.
AWS analytical services
Big data processing
Amazon OpenSearch Service
Dashboards and visualizations
Visual data preparation
AWS Glue DataBrew
For example, Amazon Redshift has gained popularity due to its speed and high cost-effectiveness. It can be used to analyze AWS data from repositories, lakes, and databases. You can also implement machine learning using Structured Query Language. Flexibility and control with a high level of security in the exchange between departments and regions are the main advantages of this AWS tool.
The best solutions with AWS
The development of research in the branch of machine learning and artificial intelligence, including with the help of AWS data engineering tools, opens up a vast field for activities. If, in the beginning, it was clean and not trampled down, then as it was used, many tracks appeared on it. It is easy to get confused in finding your way into them, so studying successful use cases of combining AWS tools for processing Big Data makes sense.
It would be good to start setting up access rights according to the concept of least privilege. In AWS, this is controlled using the IAM service and the ACL.
- AWS IAM manages administration policies from the application flow and sets them based on AWS users (groups) and their roles in the process, as well as based on resource information. Both should be checked for activity. When creating rules, it is better to avoid general permission and reduce the number of root users.
- AWS ACL restricts traffic and access rights by resource and minimizes open ports. Experienced AWS users recommend expanding the limits for service isolation and reducing the number of entry points.
The approach includes constantly checking for the location of the AWS data and the possibility of storing it securely elsewhere. For example, AWS compliance logs can be kept separate from production data. It is also worth paying attention to the thoroughness of deleting unnecessary AWS data.
Maintain optimal performance with AWS
Time-tested practices for using the ETL method can be represented as follows. Let's say there is a 4-step daily ETL workflow in which the AWS data from the source is stored in AWS S3 and then loaded into Amazon Redshift. It calculates daily, weekly, and monthly AWS data aggregations. In AWS S3 they are processed and presented to the customer conveniently, for example, using AWS Redshift Spectrum and Amazon Athena.
Cost optimization for AWS tools
Using AWS data engineering has many benefits but comes at a cost. Moreover, there are a lot of economic factors influencing the final price. You can cut costs in two ways:
- Proper AWS account setup
- automate the creation and management of account or posting groups with AWS Organizations, making it easier to review consolidated invoices
- separation of production and development AWS workload
- using labels — activating cost allocation with AWS Cost Explorer or external tools
- use AWS (CUR) for cost reporting — an hourly report doesn't cost much
- use the AWS Cost Explorer tool; it also shows the activities that generate costs
- Smart cloud asset management
- find an AWS Savings Plan that matches your activity. It will give you the flexibility to workload and keep your costs under control
- scheduling a working virtual environment on and off on weekdays saves two-thirds of running the cost
- clean up snapshots of stored AWS data periodically; usually, only the last one is needed
- get rid of other zombie resources
- move infrequently used AWS data to lower storage levels, to the "glacier"
- re-architect AWS in a more cost-effective way
The cost amount always depends on the budget, but it must be justified for any size.
Successful cases of cooperation between AWS and business
Using AWS services, businesses of various sizes can streamline their work with data, optimize costs and implement their business strategy to the maximum.
Reducing the cost of AWS services
Toyota Connected Corporation's data comes from millions of connected vehicles and is stored in the Amazon S3-based data lake of the same name. Interactive SQL queries and machine learning applications use open-source analytics frameworks such as Apache Spark, curated through Amazon EMR to solve large distributed data processing workloads. With the help of the service, a balance was found between cost and efficiency, as well as fault tolerance for millions of partitions in the lake.
Using Machine Learning
An example is AWS' collaboration with ENGIE, an energy group focused on low-carbon energy and related services. Leveraging AWS enables digital transformation with Common Data Hub and runs over 1,000 machine-learning models to serve their power plants.
Cloud service convenience
U.S. travel company Expedia improves operational agility and resiliency by moving to AWS. The brand will carry 80% of mission-critical applications from on-premise data centers to the cloud. Using AWS, Expedia Group has become more resilient, and developers have been able to innovate faster with millions of dollars in savings. The company provides travel booking services on the main site and about 200 subsites.
This fuel will heat for a long time
AWS data engineering moves humanity forward in development because it helps businesses. With data analysis, it is possible to optimize the processes of mass use (sales, travel, education). On the other hand, using tools and techniques for working with Big Data allows one to identify and eliminate miscalculations in the business scheme and eliminate them.
In the system of market relations, demand determines the supply and cost of goods and services. According to the Dice Tech Job Report 2020 (it is also an analysis of Big Data), in 2019 the market for data engineering services increased by half, with normal growth of up to 5%. In 2020, the pace of demand decreased (growth amounted to a quarter) but continued to outperform other professions vastly. In the future, market researchers expect this trend to continue.
It means that consumers need Big Data and work with it. Those who are included in this number can contact DATAFOREST specialists, who have long and deeply studied this industry and put into practice solutions recognized by customers as successful.
Shelves with AWS tools
AWS Data Engineering is represented by a wide range of tools responsible for many workflows. These tools are good because they can be sharpened to the specific enterprise data needs of the user. The most frequently used in practice are tools for the following tasks:
- Data entry – the ingestion of heterogeneous unstructured information into a single certified AWS repository from different sources; this is the most time-consuming procedure in AWS Data Engineering, but thanks to the platform, it is performed efficiently and quickly
- Storage — AWS provides storage solution options to suit your specific needs. They are correctly combined with other processing tools and reduce the cost of this process
- Integration tools — the ETL and ELT methods of data integrity are collected and centrally presented using Data Ingestion Tools; here, the time to move data is added to the complexity of the process, which is also implemented quite simply through AWS
- Data warehouse — unlike data storage, lakes, information is stored purposefully and structured by warehouse tools, which contributes to query optimization
- Visualization — the final stage of AWS Data Engineering is supported by business intelligence tools, whose work is focused on producing graphics that are easy for humans to understand
In addition, AWS Data Engineering Services can optimize the cost of using the above groups of tools — individually and collectively in a project build.
Perfection knows no limits
Once you've successfully built a simple pipeline using AWS CodePipeline, you can move on to a more complex four-step pipeline. It can use the GitHub hosting service repositories as input. Then development and testing can be entrusted to the Jenkins integration service. The AWS CodeDeploy software deployment automation service will prepare the system for using the code data on the intermediate server.
What is AWS data engineering, and why is it important?
It is a set of Amazon data platform services for preparing disparate data from different sources for a single analytical study.
What are some standard AWS data engineering services?
These are Amazon S3, Amazon Kinesis, AWS Glue, AWS CloudWatch, Amazon Redshift, Amazon IAM, AWS Lambda, Amazon EMR, Amazon Athena.
How can I get started with AWS data engineering?
You must familiarize yourself with account setup practices, explore the management console, and understand how to control costs.
What is the process for building a data pipeline on AWS?
It is a methodology for creating a sequential set of data processing elements, where the output of one part is the input of the next. The components of the conveyor are executed in parallel or with a division in time.
What are some best practices for AWS data engineering?
It is the optimal combination of AWS tools and the sequence of their use for collecting, transforming, storing, and visualizing the received data for a specific business case, which can be used for other similar issues.
How does AWS ensure data security and compliance?
AWS Compliance provides insight into the strength of the platform's controls to secure and protect data in the cloud.
How does AWS help manage costs and resources for data engineering?
AWS allows you to reduce costs in two ways: by properly setting up your account and by using cloud management tools.
What types of data sources can be ingested into AWS for data engineering?
These are media structures, cloud data, web pages, the Internet of Things, and Big Data databases.
What tools are available for data visualization and analysis on AWS?
AWS uses two primary visualization tools to create easy-to-understand reports — Amazon Managed Grafana and Amazon QuickSight.