Big Data Engineering is one of the essential tasks for any data-driven organization to gain an edge over its competitors. With the increasing trend of data generation across the world, managing information has become a challenging task for organizations. Analyzing Big Data is not a straightforward process of collecting, storing, and processing data.
It requires sophisticated tools, the right experts, and complex algorithms. To ensure organizations harness the power of data, there is a need for Big Data Engineering. Companies employ Big Data Engineers to manage Big Data, which could become foundational for Data Science initiatives.
Without Big Data Engineering, companies will struggle to develop a data culture that would hinder their overall business operations. In this article, you will learn about what is Big Data Engineering, what are the steps involved, the skills required, the role of a Data Engineer, and how they are different from Data Analysts and Data Scientists.
Table of Contents
What is Big Data?
A colossal amount of information is called Big Data. Since the digital revolution, the world has witnessed an increase in the number of data generation sources. This has led to the collection of a plethora of data. Over the years, data collection was mostly a manual process, where professionals used to enter values into spreadsheets.
This resulted in a collection of Structured Data that mainly consisted of numbers and short text. However, due to the proliferation of digital products, data collection has become automatic, at least with individual digital solutions.
Today, every digital product has its own database that assists in collecting and processing data automatically. But, integrating different data sources still does not happen out of the box. APIs are required to request data and collect it at a single location to further process information and perform data analyses.
While it is easier to collect data from different sources that allow access through APIs, massive amounts of data are present on several portals that would require web scrapping to gather information. Screen scraping is performed to collect data from public sources to enrich data for better profiling or to enhance insights generation, leading to more data within organizations.
Since data comes from different sources and are of different types, i.e., Structured and Unstructured data, organizations have to deploy various techniques to address their challenges.
What is Big Data Engineering?
Data collected from different sources are in a raw format, i.e., usually in a form that is not fit for Data Analysis. The idea behind what is Big Data Engineering is not only to collect Big Data but also to transform and store it in a dedicated database that can support insights generation or the creation of Machine Learning-based solutions.
Data Engineers are the force behind Data Engineering that is focused on gathering information from disparate sources, transforming the data, devising schemas, storing data, and managing its flow.
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Steps Involved in Big Data Engineering
Now that you have understood what is Big Data Engineering. Let’s read more about the steps involved in it. Big Data Engineering is a strenuous process that involves a lot of effort since data requirements within organizations can change and hence the data handling process. However, there are a few standard processes that are essential for any Big Data Engineering initiatives, which are as follows:
- Data Collection: Data collection is carried out to gather relevant information for catering to business needs. Before starting the collection of data, there is a need to assimilate business requirements. Once the data need is defined, Data Engineers integrate internal and external data sources to accumulate necessary information.
- Data Lake Storage: It is a data pool that stores different types of data — Structured or Unstructured — in a raw form. Several data sources are integrated with Data Lakes to aggregate information for simplifying the further process of Data Engineering. A Data Lake is a cornerstone for any data-driven company that works with Big Data.
- ETL: It stands for Extract, Transform, and Load. The primary objective of ETL is to extract data from a Data Lake or other data sources, then transform it into different analytics-ready forms, and load it into a Data Warehouse. The transformation in ETL includes data discovery, mapping, code generation, execution, and data review.
ETL is one of the most crucial steps involved in Big Data Engineering, which assists in converting the raw data into meaningful ways by enhancing the quality of information. The effectiveness of insights garnered through data analyses can be highly correlated with how well an ETL is performed.
- Data Warehousing: After Data Transformation through ETL practices, the data is stored in a Data Warehouse, a data management system that enables data analysis with Structured or Semi-structured data. Data Warehouse accelerates analytics with faster throughput of queries, allowing companies to process a vast amount of data for uncovering insights quickly.
Over the years, Cloud-based Data Warehouses like Amazon Redshift have evolved to offer better caching and querying directly into the AWS S3 Data Lake to expedite analytics workflows.
- Data Management: Big Data often leads to Data Silos without proper Data Management. Data Silos are data storage that has been ideal for a more extended period of time. Usually, the rate at which data is analyzed is lesser than the rate at which information is collected. Consequently, a lot of data is left unanalyzed due to a lack of Data Management.
Data Engineers are also responsible for overseeing how data is being used and devising new ways to avoid Data Silos. While Data Silos are one aspect of Data Management, controlling access to information is another essential part of Data Management.
To comply with the new data privacy regulations and avoid data breaches, Data Engineers implement best practices to control the access of information across organizations.
Skills Required For Big Data Engineering
Does the first question arise what are Big Data Engineering requirements? It requires the execution of various tasks, it is a skill-intensive process. As a Data Engineer, you will need to master the following:
- Data Structure: Although the reliance on Structured Data is still prominent among companies, the trend of generating insights from Unstructured Data has been gaining prominence. Data Engineers need to handle different data types to ensure companies accomplish their objectives with both types of data.
While Data Warehousing helps in fulfilling the requirements for Structured Data, Unstructured Data is directly queried by Data Scientists from Data Lakes. Data Engineers should organize Unstructured Data in a way that is easy to locate within Data Lakes for further analysis.
- SQL: Structured Query Language (SQL) is probably the most widely used tool for reading and writing information into databases. With SQL, Data Engineers can connect with almost every Relational Database to extract and load data efficiently. SQL also assists in creating the desired schema that would accelerate data handling processes for different business-critical tasks.
Since Data Engineering involves working with numerous databases, SQL has become an essential language for simplifying Big Data Engineering tasks.
- Python: Data Engineers are responsible for cleaning data to remove outliers and unknown characters, split information, enhance data, and other complex tasks. Python is the most popular programming language for Big Data Engineering, Data Science, and Data Analysis.
In other words, Python is a must for any data-related tasks. Being proficient in Python programming can simplify several Big Data Engineering tasks with the help of libraries like Pandas, NumPy, Matplotlib, etc.
- Big Data Tools: Traditional Data Handling techniques failed to process Big Data since it requires the support of extensive computation and performance speed. As a result, several Big Data tools were introduced, such as Apache Hadoop, Apache Spark, Apache Kafka, etc.
These Open-Source solutions allow Data Engineers to streamline the storage and processing of Big Data with concurrent processing and fault tolerance.
- Data Pipelines: Creating robust Data Pipelines is the most critical task of Data Engineers. Data Pipelines are created to ensure the best ETL practices for storing Structured Data in Data Warehouses and model development with Unstructured Data. Especially in big tech companies, professionals are required to create numerous Data Pipelines for different business initiatives.
Data Pipelines expedited the entire analytics workflow to transform data from one representation to another. It is also used in real-time analytics for quicker decision-making, making it the most vital skill in Data Engineering.
- Data Modeling: Data stored in databases have different data models to support a variety of business processes. As a Data Engineer, you should understand data models to effectively pull information and store it in either a Data Lake or Data Warehouse.
Analytics requires a different model altogether; as a result, Data Engineers should model data in a way that is suitable for analytics. One of the widely used data models for analytics is dimensional modeling, which includes Star or Snowflake schema.
How Big Data has Evolved to Data Engineering?
Data Management is the most vital factor for Data Analytics. The huge volumes of data are generated at a rapid rate and it is becoming harder to manage the complex data with traditional technologies such as Hadoop, MapReduce, Yarn, HDFS, etc. These are some of the widely used technologies that offered companies a scalable solution to manage high volumes of data. But the requirements to handle modern applications and complex is not possible with these traditional technologies.
The adoption of Cloud technologies such as Spark, Kafka, serverless, etc. has delivered a significant boost to businesses. These are the perfect tools developed to satisfy all the Data Engineering needs of a business. The uncoupling of storage and the compute delivered faster query performance and can manage the processing of multi-latency petabyte-scale data with auto-scaling and auto-tuning.
Cloud is one of the biggest disruptors o Big Data as it enabled the separation of storage and computation parts making it easier for users to scale up or scale down the servers according to the business requirements. It also helps companies cut down the cost of processing data engineering pipelines at scale.
Spark is a distributed processing engine that can help users manage the petabyte-scale of data for Big Data Engineering and enable the use of Machine Learning and Data Analytics. Spark can deliver 100x more speed than Hadoop for Data processing.
Kafka is a data streaming platform that can handle trillions of events a day and is widely used for messaging queues to a full-fledged event streaming tech.
Heavy adoption of these technologies by prominent providers such as Microsoft Azure, Amazon Web Services (AWS), and Databricks furthered the evolution of Big Data to Data Engineering.
Need of the Data Engineer
Data Engineers are responsible to make data available to Data Scientists and Data Analysts to find the right data and make sure that the data is trusted and in the right format. They also mask the sensitive data to keep the data protected. Data Engineers exactly know what is Big Data Engineering and try to optimize and restructure the data as per the business requirements so that they spend less time on data preparation, and operationalize data engineering pipelines.
Data Engineers play an important role in Data Analytics, and it designs and builds the environment necessary for Analytics.
7 Important Capabilities of Data Engineering
As companies came to know about the importance of what is Big Data Engineering. Instead of using the old methods to get better results and growth, companies shifted towards AI-driven approaches for end-to-end Data Engineering.
- Data Engineers create the Data Pipelines using enterprise-level Data Integration.
- Data Engineering helps in identifying the right dataset with an intelligent data catalog.
- Mask sensitive information such as bank details, card numbers, passwords, etc.
- Simplifies the data preparation task and allows companies to collaborate with data.
Data engineering User Personas
Though Cloud technologies are an important factor in the Data Engineering process, Data Engineers, Data Scientists, and Data Analysts are illustrative user personas of Data Engineering. Data Engineering serves a wide variety of fields such as Sales, Finance, Marketing, Supply Chain, etc. These all fields raise many questions to explore about data such as:
- How can data help me predict what will happen?
- How can data help me understand what has happened?
- How can my staff collaborate better and prepare data more easily?
Whereas, Data Analysts analyze the business data provided by Data Engineers to explore and generate insights from it. They ask the following questions about the data:
- How to know if the data is trusted?
- How to simplify the data preparation and spend more time on analysis?
- How to collaborate with other teams?
- How will I make this data available in my Data Lake?
Also, Data Scientists spend around 80% of their time preparing the data as compared to building the models. They often ask questions such as:
- How to ensure the data is trusted for modeling?
- How to simplify the data preparation and spend more time on modeling?
- How can I deploy and operationalize my ML models into production?
Why Data Engineering is Important to AI and Analytics Success?
Many AI projects fail due to the lack of correct data. Though, companies put huge investments in managing data and Analytics but still they face difficulties in bringing data into production. Data users spend 80% of the time preparing data before they can use it for analysis or modeling. Clean data is a common need for all purposes and it is the single most important factor of Data Engineering.
In this article, you learned about what is Big Data Engineering and how it is a crucial part of any data-driven organization that is trying to gain an edge over its competitors. Without proper Data Engineering efforts, companies would witness failure in projects, leading to substantial financial losses. This article provided you with an in-depth understanding of what Big Data Engineering is along with a list of steps and skills involved in an ideal Big Data Engineering process.
Most businesses today, however, have an extremely high volume of data with a dynamic structure. Creating a Data Pipeline from scratch for such data is a complex process since businesses will have to utilize a high amount of resources to develop it and then ensure that it can keep up with the increased data volume and Schema variations. Businesses can instead use automated platforms like Hevo.
Hevo helps you directly transfer data from a source of your choice to a Data Warehouse or desired destination in a fully automated and secure manner without having to write the code or export data repeatedly. It will make your life easier and make data migration hassle-free. It is User-Friendly, Reliable, and Secure.
Details on Hevo pricing can be found here. Give Hevo a try by signing up for the 14-day free trial today.