In simplest terms, engineering is science and technology in amalgamation to create something that reduces human effort and progresses humanity. Thus, engineers design and build, replicating which, data engineering involves data engineers building data pipelines to make it useable to predict any particular outcome.
As a software development and consulting company, TechVariable uses data engineering and aids businesses to transform their data into practical insights to predict market trends and grow efficiently by introducing new products, all the while optimizing costs.
The data engineering principles at play at TechVariable are listed below:
Online Analytical Processing (OLAP) vs Online Transaction Processing (OLTP)
Software systems can be broadly categorized as OLAP and OLTP. This categorization answers the need for Data Engineering.
Online transaction processing (OLTP) collects, processes, and saves data from real-time systems such as banking and e-commerce. Online analytical processing, or OLAP for short, analyses aggregated historical data from OLTP systems using complicated queries.
So, if a business intends to make data-driven decisions (at present or in the future), they need to build an OLAP system.
With the increasing usage of analytics and machine learning across the industry, every business is integrating OLAP systems and here data engineering plays an important role.
It is responsible for creating a pipeline that handles large-scale data from different sources from OLTP systems and makes it ready for analytics and machine learning. So, in a sentence, Data Engineering is the bridge between OLTP and OLAP systems.
Database vs Data Warehouse vs Data Lake
A collection of data or information is known as a database. Databases are typically electronically accessible and support online transaction processing (OLTP). In general, a database can be divided into two segments:
- Relational databases – that store data in tables with fixed rows and columns.
- Non-relational databases (also known as NoSQL databases) – that store data in various formats, including JavaScript Object Notation (JSON), binary JSON (BSON), key-value pairs, tables with dynamic rows and columns, and nodes and edges.
Common databases include PostgreSQL, MongoDB, etc. Databases are generally not optimized for pulling and pushing large amounts of data in one go, thus necessitating a data warehouse.
A data warehouse is nothing but a large database, being optimized for analytics. It analyzes, searches for insights, and creates business intelligence (BI) through reports and dashboards. Some of the most popular data warehouses in the industry are BigQuery, Redshift, Snowflake, and Azure DW.
So, what’s the basic difference between a database and a data warehouse?
Databases store the most frequently used and recent data, while data warehouses typically store all the current and historical data.
Before saving the data in data warehouses, data engineers do some form of processing and cleaning for efficient storage. However, at times, valuable information gets removed. This is where Data Lake comes into play.
To nullify this problem, Data Lake stores all kinds of data irrespective of size, structure, format, and occurrence. Data is not pre-processed before being stored in a data lake. The data scientist and analysts can use this data lake for analytics and predictive modeling without missing crucial information.
Now, a properly coordinated analytics team and data engineering team can ignore the necessity of a data lake and stick to the data warehouse, as both teams will be on the same page about what data to keep and what not to keep.
ETL vs ELT vs Data Engineering
People often use the terms ETL, ELT, and Data Engineering interchangeably. Though not wrong completely, there are certain key differences.
ETL stands for Extract, Transform, and Load, while ELT stands for Extract, Load, and Transform. Extraction is similar for both processes. Difference seeps into what is to happen after extraction.
We have two options – either load the data to a data lake and then process or transform it, or first transform it into a structured schema and then load it into a data warehouse. ELT is more favorable in this case, as there is flexibility in transforming the data to choice directly from the data lake.
ELT vs ETL
Now let’s address the elephant in the room. How is data engineering different from ETL and ELT?
The internet is divided into two factions regarding this question. Some advocate that there is no difference, while others say there are differences. Here’s what we think about it.
An ETL/ELT developer mostly uses GUI-based third-party tools like Informatica. But a data engineer will always use a programming language like Scala/Java/Python on top of cloud or open-source frameworks like Glue, Spark, etc. Hence, a data engineer has more flexibility in making choices and a better understanding of what is happening under the hood.
Data Engineering Stages or Data Pipeline
A data engineering pipeline contains multiple stages which vary on a case-to-case basis. However, the following standardized steps are followed most of the time:
- Collecting data requirements, such as how much data should be retained, how it will be utilized, and when and to whom access is required.
- Specialized technologies tailored for data processing for specific needs, using tools to access data from different sources, data modification, enrichment, transformation, and data storage in a storage system, optimized for the particular use of data such as relational database, NoSQL, Handop, Amazon S3, or Azure blob storage.
- Maintaining metadata about the data, such as the technology that handles the data, the schema, the size, the technique of data security, the data source, and the eventual owner of the data.
- Ensuring data security and governance through utilizing centralized security controls such as LDAP, encrypting data, and auditing data access.
The above steps can also be represented as:
- Acquisition: Obtaining data from many systems
- Cleansing: Error detection and correction
- Conversion: Data conversion from one format to another
- Disambiguation: Data interpretation with numerous meanings
- De-duplication: Getting rid of duplicate data copies
- Storing: Storing the data in an optimized data warehouse
Next, let’s discuss the data engineering tools used in the industry for the steps mentioned above.
Data Engineering Tools is an umbrella term that cumulatively describes the tools used in a modern data stack, which helps save time to create data integration. These stacks can contain both open-source and proprietary tools.
- Data Ingestion tools collect data from a variety of sources automatically and enable the transfer of such data streams into a single storage place. Most of the ETL/ELT tools can do this job. Examples include Apache Spark, AWS Glue, etc.
- Data Transformation tools enable good data analytics. These tools convert data from one format to another. Some examples are Apache Spark, AWS Glue, and Azure DataBricks.
- Data Destination Tools or Data Warehouses such as Azure Synapse Analytics, Google BigQuery, and Amazon Redshift, should be cloud-based for two reasons:
- It’s an upgrade over an on-premises legacy database.
- It is scalable and has off-the-shelf deployment capability.
- Data Visualization tools or Business Intelligence tools are the gateways to answers. BI solutions assist in making data-informed decisions to reduce operational risk and maximize efficiency in operations enablement. Power BI, Tableau, Kibana, and Grafana are a few examples.
It is always best to go with a cloud-based, fully managed data engineering tech stack. This helps the developer to focus on what to do with the data rather than wasting time on the scalability and security of the infrastructure.
Our list of favorite tools is Apache Spark, AWS Glue, Azure Databricks, AWS Redshift, Snowflake, Grafana, Azure BI, and AWS Quicksight.
Data Pipelines
The most common types of data pipelines are:
- Batch: When a company needs to move a large amount of data on a regular basis, it is common for them to forego using a batch processing system. This data is not transmitted in real-time by a batch data pipeline. Some businesses, for example, will use this system to combine marketing data into a broader system for subsequent analysis.
- Real-Time: In a real-time data pipeline, the data is processed virtually instantly. When a corporation wants to process data from a streaming location, such as connected telemetry or the financial market, this solution comes in handy.
- Cloud: AWS bucket data is optimized for use with cloud-based data. These technologies can be hosted on the cloud, which allows a business to save money on resources and infrastructure. To host the data, the organization relies on the expertise of the cloud provider.
AWS Data Stack
Azure Data Stack
- Open-Source: An open-source option is a low-cost alternative to a data pipeline. These open-source data engineering tools are less expensive than commercial cloud-based systems, but they require knowledge to utilize. Since the tools are provided to the public for free, other users can alter them.
Our services:
We work on helping businesses in:
- Data Infrastructure Modernization – migrating existing data pipelines to a modern, secure big data pipeline driven by major cloud providers that guarantee scalability, minimal maintenance, security, 24×7 uptime, and low latency wherever in the world.
Tech stack: AWS Kinesis, Azure Event Hub, AWS DynamoDB, AWS Redshift, Azure CosmosDB
Use case:
Assume we wish to launch a new social network. Because social networking solutions include multiple components, we’ll call it a microservice architecture. Because a vast amount of text, image, and video information will be generated (in technical terms, this is referred to as data volume), we want a robust, scalable infrastructure to adequately store this enormous volume of data.
This is where cloud big data enters the arena. We can stream and save data from AWS Kinesis and AWS S3 because the data is continuously generated in significant volumes. AWS Kinesis can set up a failsafe system for communicating between various microservices, while S3 can keep enormous amounts of incoming data indefinitely.
- ETL – collecting raw data from many sources and transforming it into a more organized, structured, and relevant representation that saves storage and database space while consolidating all data conveniently.
Tech stack: Azure Databricks, Azure Data factory, AWS Glue.
Use case:
Let’s say a fintech startup is offering cost-tracking services to businesses. For this purpose, they are giving businesses some connectors to work with their existing operational tools.
One of their customers uses Salesforce to manage their costs and earnings from each online sale. Also, they are using Google Sheets to store their offline sales data. Thus, the data will be laden with errors or of different formats due to multiple data sources and manual entry in the case of offline sales. So, to get accurate KPIs, we need a common platform. This is where ETL comes into play.
With Azure Databricks/Azure Data factory/AWS Glue, we can easily extract the data from multiple sources, pre-process them to remove unwanted information, restructure, add additional data based on algorithms, and finally combine them to keep them safe in distributed storage in the cloud. This data is then used to extract key insights.
- Cloud Data Analytics – using cloud-based, industry-leading solutions to obtain real-time business value insights and optimized cost per KPI.
Tech stack: AWS Quicksight, Azure Synapse Analytics, PowerBI, Tableau, Grafana.
Use case:
Whether in edtech or fintech, the importance of analytics cannot be overstated – we must help business people visualize and understand our performance, sales, and growth areas.
Assume we’re in the edtech space. With multiple students and institutions under our business, we will need to provide them with an attendance ratio, average assignment completion rate, exam score for each student, etc. These statistics, however, need to be more self-explanatory to businesspeople. As a result, we require a visual depiction of these numbers.
This is where PowerBi comes in. We may continuously update the metrics as new data arrives, which is then shared with the relevant parties to convey our potential.
