The process of data ingestion is extremely important in any data-oriented task. This is the initial phase of transferring your data from its source to the destination, and it’s essential to guarantee that you possess the accurate details at the appropriate moment.
The crucial aspect of data ingestion is being aware of the type of data required for your intended destination and comprehending how that data will be utilized by that destination.
What is Data Ingestion?
The act of bringing data into a system is referred to as Data Ingestion. Without a doubt, it is one of the most essential components of any data analytics workflow. In order to accomplish this, businesses must acquire data from a variety of sources, such as email marketing services, CRM systems, financial applications, and social media networks.
Performing data ingestion usually falls under the responsibility of data scientists who need proficiency in machine learning and programming languages such as Python and R.
Data Ingestion vs. ETL
Data ingestion and ETL may sound similar, but they are distinct processes. Data ingestion pertains to the act of importing data into a database or storage engine whereas ETL involves the process of extracting, transforming, and loading data.
Their similar names and frequent coincidence often result in confusion about the distinction between the two.
The primary distinction between data ingestion and ETL lies in their respective functions.
Data ingestion refers to the transfer of data from an external source, such as a database, to a different storage location, such as another database, typically without altering the data.
Suppose you possess a set of files in an Amazon S3 bucket that you want to incorporate into your database. In that case, you would need to perform data ingestion to transfer those files to your database location.
Extract transform load, also known as ETL, is a method of extracting data from one system, transforming it, and then loading it into another system for utilization.
In this instance, instead of merely transferring data from one place to another without any modifications.
Data Ingestion vs. Data Integration
The act of transferring data between systems can be described as data ingestion and integration. Specifically, data ingestion pertains to the act of adding data to a database, while data integration involves retrieving that same data from a database and inserting it into a different system.
One may need to integrate data when seeking to utilize a product from a different company in conjunction with one’s own product, or when merging internal procedures with external entities.
The discrepancy between the two expressions is based on their respective definitions.
The process of bringing data into a database or storage location is known as data ingestion. This normally requires the usage of an ETL (extract, transform, load) tool to relocate data from one system (such as Salesforce) into another repository, such as SQL Server or Oracle.
Data integration involves consolidating several datasets into a single dataset or data model, which can be utilized by various applications, specifically those from varying vendors such as Salesforce and Microsoft Dynamics CRM.
Types of Data Ingestion
The process of data ingestion entails the gathering, cleansing, transforming, and integrating of data from diverse sources into one unified system within a data warehouse, in order to enable its analysis.
Data ingestion can be broadly categorized into two main types.
- Real-time ingestion involves streaming data into a data warehouse in real-time, often using cloud-based systems that can ingest the data quickly, store it in the cloud, and then release it to users almost immediately.
- Batch ingestion involves collecting large amounts of raw data from various sources into one place and then processing it later. This type of ingestion is used when you need to order a large amount of information before processing it all at once.
Data Ingestion Tools
Any organization heavily relies on data ingestion tools which are software products that collect and transfer data in structured, semi-structured, and unstructured formats from one destination to another. Since they automate the ordinarily challenging and manual ingestion procedures, organizations can devote their time to using the data to enhance business decisions and reduce data movement time.
Data is moved along a data ingestion pipeline, a series of processing steps that take data from one point to another. The pipeline might start with a database or other source for raw information, then pass through an ETL tool that cleanses and formats it before moving it on to a reporting tool or data warehouse for analysis.
In today’s digital economy, it is essential for businesses to possess the capability to rapidly and effectively assimilate data in order to remain ahead of the competition.
Data Ingestion Framework
The components of the data ingestion framework (DIF) comprise a range of services that facilitate the import of data into your database. These components encompass:
- The data source API enables you to retrieve data from an external source, load it into your database, or store it in an Amazon S3 bucket for later processing.
- The data source API proxy provides an interface between your application and the data source API. This proxy acts as a gateway between your application and other AWS services, enabling your application to access resources such as Amazon S3 buckets without requiring credentials or further authorization details from you.
- The data source service contains all of the code required to interact with external data sources through one or more APIs using a method similar to web browsing (for example, GET requests).
Data Ingestion Best Practices
Collecting data through a properly designed and executed data pipeline may require considerable time and dedication. It is necessary to have additional measures in place for data collection and ensure that it is done in a manner that would facilitate its future use by the team. Below are some recommended procedures for the acquisition of data:
- Collect only the data you need at each stage of the process. It will save time and money because you won’t have to reprocess anything later.
- Make sure each collected data piece has an associated timestamp or unique identifier so that it can be matched up with other parts of information later on in your analysis process. It will also help ensure accuracy in your final results.
- Create a well-structured format for each piece of information so that anyone who needs access can easily find what they’re looking for later on.
BONUS: How to Learn Big Data?
Big data refers to a vast quantity of information, encompassing anything and everything, even a single post on Facebook, that is generated on a daily basis by individuals.
The rate of data growth is remarkable; an estimate predicts that by the year 2025, the world will generate about 463 exabytes of data daily, which is equivalent to 212,765,957 DVDs per day.
Big data analytics is utilized to handle and analyze a massive volume of data that is not structured properly. This data can include various forms, such as audio, text, images, and others.
Big data analytics involves discovering valuable patterns from vast amounts of unstructured data through various stages ranging from data cleansing to pattern identification. It necessitates storing and processing massive data quantities. The three V’s define big data.
- Volume- It refers to the size of data, which means how much data is generated.
- Variety- It refers to the type of data, which means which type of data is generated like structured data or unstructured data.
- Velocity- It refers to the speed of data, which means at what speed data is generated.
Next, we will proceed to the systematic guide to Big Data.
How to Learn Big Data Step by Step?
Step 1- Learn Unix/Linux Operating System and Shell Scripting
Having strong skills in shell scripting is important because numerous tools utilize a command line interface that relies on shell scripting and Unix commands.
Data pipelines can be constructed through the assistance of Shell Scripting, which involves a series of commands written in a text file designed for a UNIX-based operating system.
These resources are available for you to acquire the knowledge of Unix/Linux Operating System and Shell Scripting.
- Linux Command Line Basics (FREE Course)
- Shell Workshop (FREE Course)
- Configuring Linux Web Servers (FREE Course)
- Linux Fundamentals (Coursera)
- Introduction to Bash Shell Scripting (Coursera Project)
Step 2- Learn Programming Language (Python/Java)
Java remains the foundation for a multitude of big data frameworks due to the fact that various vital core modules in widely-used big data tools are coded using Java.
Big Data processing can also be achieved through Python, although Java has a more straightforward approach that doesn’t require external assistance.
The decision of whether to learn Java or Python is yours to make.
Hadoop is Java’s framework for developing big data applications, while Python boasts numerous open-source libraries and tools. For those new to programming, Python is an excellent choice since it is relatively simple to understand and implement. If you have experience, however, Java may be better suited for your needs.
Next, we will explore the available learning materials for Java and Python.
- The Python Tutorial (PYTHON.ORG)
- Python for Absolute Beginners! (Udemy)
- Python for Everybody (Coursera)
- Python 3 Tutorial (SOLOLEARN)
- CS DOJO (YouTube)
- Programming with Mosh (YouTube)
- Corey Schafer (YouTube)
- Python Crash Course (Book)
- Java Programming Basics (Free Course)
- Become a Java Programmer (Udacity)
- Become a Java Web Developer (Udacity)
- Core Java Specialization (Coursera)
- Introduction to Java (Coursera)
- Java Programming Masterclass covering Java 11 & Java 17 (Udemy)
Step 3- Learn SQL
Strong comprehension of SQL is highly essential as it is the most sought-after aptitude for effectively handling large amounts of data. Additionally, familiarity with NoSQL is necessary as unstructured data may need to be managed.
Experimenting with SQL in relational databases enables us to comprehend how to extract information from extensive data collections.
- Learn SQL Basics for Data Science Specialization– Coursera– This specialization program is dedicated to those who have no previous coding experience and want to develop SQL query fluency. In this program, you will learn SQL basics, data wrangling, SQL analysis, AB testing, distributed computing using Apache Spark, and more.
- Excel to MySQL: Analytic Techniques for Business Specialization– This Specialization program is offered by Duke University. This is one of the best SQL online course certificate programs. In this program, you’ll learn to frame business challenges as data questions. You will work with tools like Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights.
- W3Schools– You can learn DBMS and its concepts from the Free Tutorial of W3Schools.
- NoSQL systems– In this course, you will learn how to identify what type of NoSQL database to implement based on business requirements. You will also apply NoSQL data modeling from application-specific queries.
Step 4- Learn Big Data Tools
After achieving proficiency in Python, Java, and SQL, the next logical progression is to gain expertise in Big Data technologies which include Hadoop and MapReduce, Apache Spark, Apache Hive, Kafka, Apache Pig, as well as Sqoop.
- Intro to Hadoop and MapReduce(Udacity)- This is a completely Free Course to understand the concepts of HDFS and MapReduce. In this course, you will learn what is big data, the problems big data creates, and how Apache Hadoop addresses these problems.
- Spark (Udacity)- This is another completely Free Course to learn how to use Spark to work with big data and build machine learning models at scale, including how to wrangle and model massive datasets with PySpark. PySpark is a Python library for interacting with Spark.
- Hadoop Developer In Real World (Udemy)- This course will cover all the important topics like HDFS, MapReduce, YARN, Apache Pig, Hive, Apache Sqoop, Apache Flume, Kafka, etc. The best part about this course is that this course not only gives basic knowledge of concepts but also explores concepts in deep.
- Big Data Specialization (Coursera)– In this specialization program, you will get a good understanding of what insights big data can provide via hands-on experience with the tools and systems used by big data scientists and engineers.
Step 5- Start Practicing with Real-World Projects
Congratulations on mastering Big Data skills! It is now time to delve into Real-World projects, as they are crucial to securing a Big Data Engineer position.
As you undertake more projects, you will gain a deeper understanding of Data and these projects will also enhance your Resume.
To gain knowledge, Twitter provides accessible APIs for obtaining real-time streaming data from social media platforms which you can begin with.
That concludes it! By following these steps and acquiring the necessary skills, you’ll be unstoppable in your pursuit of a career in Big Data.