How to Use AWS to Implement Data Engineering?

Many small and midsize businesses use analytics to better understand their operations, cut expenses, and expand their reach. Some of these businesses may aim to establish and maintain an Analytics pipeline but will reconsider once they realize how much money and technical expertise it will require. Data engineering solutions is a valuable asset for every business. And they are adamant about not sharing this asset with outside parties, fearing that they may lose their competitive advantage. Enterprises must construct and maintain their own data warehouses and accompanying infrastructure to get the most out of intelligence harvesting.

The analytics community is humming with discussions about Machine Learning applications, which have complicated requirements including storing and processing unstructured streaming data. Instead of focusing on complex analytics, businesses can get a lot of value just by implementing effective reporting infrastructure. Because a lot of SME activity is still done at the batch data level, this is the case. From an infrastructure standpoint, cloud providers such as Amazon Web Services (AWS) and Microsoft Azure have simplified things considerably. Companies have been able to construct an accurate, strong reporting infrastructure (more or less) independently and cost-effectively as a result of this. This post is about a specific lightweight data engineering services utilizing AWS that would be ideal for a small business. By the time you've finished reading this, you'll have

• Learn the fundamentals of a simple Data Engineering pipeline.

• Learn about a specific type of AWS-based analytics pipeline.

• Use design thinking to solve a similar situation you may have.

Data Pipeline for Analytics:

Data on SMEs' business activities is maintained in a variety of locations. One of the major issues in analytics is bringing it all together so that a broad picture of the company's health emerges. SMEs can make significant advantages by gathering data from many sources, storing it in a structured and accurate manner, and then leveraging that data to develop reports and visualizations. This is how it may look from a process standpoint:

However, in terms of business activity effort, it's more like:

What's remarkable is that, while the first two components of the process cost the greatest time and effort, value is realized in the Analyze component when seen from a value chain perspective.

The oddly reversed link between effort and value keeps SMEs wondering if they will see the expected returns on their investment and save money. While today's analytics may appear to be all about Machine Learning and cutting-edge technology, SMEs may gain a lot of value from very simple analytics like:

• For leadership, a time series graph of business activities is shown.

• A updated, filterable dashboard for the sales team, displaying the top ten clients over a specified time period.

• Visualization of sales growth over time as a bar graph

• For the Operations team, an email blast is sent out every morning at 8:00 a.m., illustrating business activity expense over a specific time period.

Many of the strategic difficulties that SMEs encounter, such as business reorganization, cost control, and crisis management, can only be solved with correct data. Having a cloud-based Analytics data pipeline allows businesses to make cost-effective, data-driven decisions. These can contain both strategic C-Suite decision-making and business-as-usual indicators for the Operations and Sales teams, allowing executives to track their progress. In a word, an Analytics data pipeline allows executives to access company data. This is useful in and of itself because it allows for metrics monitoring (including the derived benefits like forecasting predictions). That's it, folks: a compelling case for SMEs to try their hand at establishing their own analytics pipeline.

The pipeline's mechanics are as follows:

Before we discuss vendors and the benefits they provide, consider this: there are as many methods to design an Analytics pipeline as there are stars in the sky. The task at hand is to build a data pipeline that runs on a secure cloud platform. It's critical to employ cloud-native compute and storage components to make the infrastructure simple to set up and maintain for a small business.

Source data for SMEs is often in the following formats:

• Payment information is kept in an Excel spreadsheet

• APIs are being used to collect data about business activity

• Third-party interaction exported as a.CSV file to a cloud storage service such as S3

When using AWS as a platform, SMEs can use AWS Lambda's serverless compute functionality to import source data into an Aurora Postgres RDBMS. Lambda supports a variety of programming languages, including Python, which is commonly used. Lambda's entire runtime in 2016-17 was five minutes, which was not nearly enough for ETL. The time limit was raised to 15 minutes two years later. This is still insufficient time to complete most ETL processes, but it is sufficient for SMEs' batch data import needs.

Lambda is typically hosted behind a private subnet within an enterprise Virtual Private Cloud (VPC), but it can communicate with third-party source systems via NAT and Internet Gateways (IG). Python's libraries (such as Pandas) make it simple to work with tabular data. The output dataframe from Lambda is saved to a table in the Aurora Postgres Database once it has been processed. The Aurora prefix refers to the AWS version of Postgres Database. Because most data is in Excel-style rows and columns anyhow, using a plain relational database makes sense, and reporting engines like Tableau and other BI tools function well with RDBMS engines.

Figure 5 depicts the AWS aspects of a data engineering solutions in greater depth. Companies that use AWS must share security responsibilities such as:

•  Using a VPC to host AWS components

• Determining which subnets are public and which are private

• Ensuring that IG and NAT Gateways are capable of allowing components on private subnets to communicate with the internet.

• Making the database inaccessible to the public

• Creating a specialized EC2 instance to send web traffic to this inaccessible database.

• Setting up security groups for the EC2 public subnet (Lambda in private subnet and Database in DB subnet)

Keeping the pipeline running:

New data is ingested using CloudWatch rules and a timed Lambda execution. CloudWatch uses Chron expression to monitor AWS resources and trigger services at predetermined periods. CloudWatch can also be used to trigger Lambda events as a SQL Server Job agent. This allows for activities with varying frequencies, such as:

• Adding a new dimension to sales activity (daily)

• Information on Operating Costs (weekly)

• Transactional activity (biweekly)

• Information regarding taxes (monthly)

Once the source file or data refresh frequency is known, CloudWatch can deploy a specific Python script to Lambda (that gets data from the source, does appropriate transformations, and loads it onto a table with defined structure).

Moving on to Postgres, you may use a mix of Lambda and CloudWatch to execute its unique Materialized View and SQL Stored procedure functionality (which enables for additional processing). This method is useful for propagating base data into deformalized, wide tables that can store company-wide sales and operations data after it has been refreshed.

The name of the game is precision:

So you've got an AWS architecture that's really secure and reliable, well-tested Python code for Lambda executions, and a not-so-cheap BI tool subscription. Are you ready to go? Not at all. If inaccuracy sneaks into the tables during data refresh, you might just miss the bus. The accuracy of the numbers displayed on a dashboard is only as good as the dashboard itself. Make sure that the schema tables you've created have the metadata columns you'll need to spot erroneous or duplicate data.

Conclusion:

We adopted a narrow-angle approach to a specific data engineering services in this post. We witnessed the Effort vs. Return spectrum in the Analytics value chain, as well as the value that can be extracted by utilizing Cloud choices. We saw the benefit in providing informative interactive dashboards to C-suite leaders and company executives.

We looked at how to develop a simple AWS cloud-based Data Engineering pipeline that can be adopted by small businesses. We discussed the architecture and its various components, as well as the features of pipeline operation, and the importance of accuracy in reporting and analysis.

Although this essay focused on one specific implementation, the goal is to communicate the idea that generating value from an in-house Analytics pipeline is easier now than it was a decade ago. It doesn't take long to explore and utilize the value hidden in data, thanks to open source and cloud tools that make the journey simple.