Cloud Data Ingestion Simplified 101

The surge in Big Data and Cloud Computing has created a huge demand for real-time Data Analytics. Companies rely on complex ETL (Extract Transform and Load) Pipelines that collect data from sources in the raw form and deliver it to a storage destination in a form suitable for analysis. However, the initial stage of this ETL process requires Data Ingestion.

Data Ingestion is simply the process of extracting data from multiple sources and accumulating it in a processing environment. This process facilitates Data Integration and also provides standalone applications such as Data Replication, Log Transfer, etc. Businesses can either develop their Data Ingestion Pipelines or can opt to choose a third-party tool to fulfil their requirements. 

This article will introduce you to Cloud Data Ingestion and explain its key features. It will further discuss the importance of this process and explain the parameters that are critical for performing Data Ingestion. Moreover, the article will list 5 popular Cloud Data Ingestion tools which you can implement to take care of your data needs.

What is Data Ingestion?

Data ingestion covers the structured and unstructured data from a source into a storage system for analysis. It is typically the first phase of any data pipeline and usually occurs in real time or in batches. Tools are used by companies to discover sources, provide access to files, and send the data to where it is needed, whether through customer-built systems or through third-party platforms themselves.

Key Features of Data Ingestion

Data Ingestion is an important aspect of Data-driven business models due to the following features:

• Data Ingestion tools are designed with the objective of extracting data from numerous data sources. Moreover, they implement various protocols to ensure secure and fast data transportation & processing.
• Data Ingestion also empowers you to visualize the ongoing data flow. It provides a simple drag and drop feature that allows you to simplify & visualize vast datasets.
• Data Ingestion tools are built to accommodate scalability. This is necessary to manage the huge datasets that companies extract daily from multiple sources. Such tools allow you to add more nodes and clusters to enhance the parallelization of the Data Ingestion process.
• Data Ingestion is not limited to a certain type of data source and the right tool will allow you to ingest data from a variety of Cloud data sources and on-premise databases. Moreover, the performance of such Data ingestion tools remains intact even when the data source is changed.

Importance of Cloud Data Ingestion

• The key importance of Cloud Data Ingestion lies in its contribution to transporting information at high speeds from data sources to a data processing facility. Moreover, a powerful Data ingestion tool works on a deliberately narrow scope.
• Therefore, it allows other teams to scale up the data transfer process with high flexibility and agility. Furthermore, Cloud Data Ingestion pipelines are responsible to set the required parameters before transferring data.
• Once such parameters are in place, Data Analysts can deploy a single data pipeline to transport data to their preferred destination.

Some of the popular instances of Cloud Data Ingestion include:

• Transferring data from Salesforce to a suitable Cloud-based Data Warehouse like Amazon Redshift to further analyze it with a Business intelligence tool like Tableau. 
• Extracting data from a popular Twitter feed to perform sentiment analysis and gather real-time insights.
• Acquiring data from various sources to train Machine Learning Models and set them up for experiments.

Data Ingestion vs Data Integration: Key Differences

New users often confuse Cloud Data Ingestion with Cloud Data Integration which is related but has its individual importance and use. This section will discuss the following 3 key differences that separate these 2 processes:

1) Methodology

• Data Ingestion creates the foundation on which all the rest of ETL activities operate. It is able to manage large volumes of data and allows you to extract data from various sources in a short span of time. However, this process plays no part in Data Transformations and without modifying the data, you can not fulfill the data requirements of modern enterprises.
• This implies, Data Ingestion is useful only as a part of the ETL process and is not capable of cleansing, merging, and validating data without leveraging a Data Pipeline. 
• Data Integration on the other hand is a complete process in itself. You can use this process to extract data from sources, transform it into a useful form and store it in a Data Warehouse for analysis. In fact, Data integration, rather than Data Ingestion, remains the more useful process for most companies. 

2) Applications

• Your Sales and Marketing Teams require valuable insights in real-time to enhance their Lead Conversions. However, Data Ingestion can only bring in huge chunks of data but will fail to provide the required insights.
• Data Integration is useful in such instances as it delivers functional insights as the end result to your teams. Moreover, Data integration involves filtering and processing data before any type of analytics technique is run, thereby speeding up the whole process.
• Data Ingestion on the other hand finds application in activities related to logging and monitoring. This is important for businesses that need to save raw text data containing information about their IT environment. Furthermore, using slight modifications, you can use the Data Ingestion process to replicate your data. 

3) Priority

• Businesses that leverage Data Ingestion usually prioritizes the task of transferring data as it is from one place to another as efficiently as possible. On the other hand, since Data Integration incorporates transformation steps, it is more suited for tasks that require Data Alterations.
• For instance, you can rely on Data Integration to implement data masking for hiding sensitive information. Techniques that encrypt data by modifying its appearance can benefit from Data Integration Pipelines.

Critical Parameters for Setting Up Cloud Data Ingestion

An efficient Cloud Data Ingestion strategy requires you to first prioritize the data sources, validate individual files, and carefully route data items to their destinations. Moreover, to ensure that your Cloud Data Ingestion setup is fully functional, you must ensure the correct use of the following parameters:

• Data Velocity: This parameter tracks the flow of data in various sources such as machines, human interaction, networks, social media platforms, etc. Moreover, it allows you to maintain the bulk or continuous flow of data during ingestion.
• Data Size: This parameter measures the enormity of your incoming datasets. It allows you to gather data from multiple sources while ensuring that your Data ingestion Pipeline is scaling as per requirement.
• Data Frequency: This parameter helps you in deciding the type of processing your Data ingestion requires, real-time or batch. The real-time processing ensures that incoming data is processed immediately after its ingestion while the batch processing requires you to process a fixed-size collection of data at once.
• Data Format: This parameter helps in making your incoming data Structured, Semi-structured or Unstructured. This allows you to store the incoming data as per its type and avoid any unnecessary data loss during ingestion.

Popular Cloud Data Ingestion Tools

Using a Cloud Data Ingestion tool will save you the trouble of spending a lot of resources and time in building and maintaining an in-house data solution. Depending on your business needs, you can choose a Data Ingestion tool from the following:

1) Hevo Data

Hevo Data, a No-code Data Pipeline helps you directly ingest data from 150+ data sources (including 60+free data sources) and further load it to the  Data Warehouse of your choice in a completely hassle-free & automated manner. Hevo operates on a fault-tolerant architecture and ensures that your data travels in a loss-less manner.

Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.

Check out what makes Hevo amazing:

• Real-Time Data Transfer: Hevo with its strong Integration with 100+ sources, allows you to transfer data quickly & efficiently. This ensures efficient utilization of bandwidth on both ends.
• Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer. 
• Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
• Tremendous Connector Availability: Hevo houses a large variety of connectors and lets you bring in data from numerous Marketing & SaaS applications, databases, etc. such as Google Analytics 4, Google Firebase, Airflow, HubSpot, Marketo, MongoDB, Oracle, Salesforce, Redshift, etc. in an integrated and analysis-ready form.
• Simplicity: Using Hevo is easy and intuitive, ensuring that your data is exported in just a few clicks. 
• Completely Managed Platform: Hevo is fully managed. You need not invest time and effort to maintain or monitor the infrastructure involved in executing codes.
• Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

2) Apache Kafka

• Apache Kafka is a popular Data Ingestion software that can provide you with high-yield Data Pipelines, real-time Data Analytics, and more. This open-source platform is one of the best in terms of throughput and performs well at even very low latency. For instance, it can provide data at high throughputs in a network that works on a latency of as low as 3ms.
• The Apache Kafka software is designed in Scala and Java. You can use it to connect with external systems easily and trade data seamlessly via Kafka Connect. Furthermore, this open-source platform supports a huge ecosystem of tools and users to help new data professionals with Apache Kafka’s functionalities.

3) Apache NiFi

• Apache NiFi was designed with the objective of automating the Big Data flow among various software systems. This ETL tool offers high-speed Data Ingestion along with low latency, loss-less transfer, and guaranteed data delivery. Moreover, NiFi is able to operate both as a standalone tool or in a cluster form depending on your requirements.
• Apache Nifi is popular for its Schema-less Processing which ensures that each NiFi processor interprets the content of data provided to it. This increases the throughput and allows you to ingest huge sets of data with ease.

4) Apache Storm

• Apache Storm is another open-source tool that performs Data Ingestion tasks in a distributed manner. This framework is written in the Clojure programming language but is easily compatible with tools written in any programming language.
• This Apache tool is capable of processing 1 million tuples per second on every node. This implies it can provide you with high scalability and fault-tolerant data delivery. Another advantage of using Apache Storm is that it connects well with any queuing database. 

5) Amazon Kinesis

• Amazon Kinesis is a Cloud-based, fully managed platform that empowers businesses to perform fast Data ingestion tasks. The platform offers services for ingesting, processing, and saving both video and data streams using its Kinesis Streams feature.
• This Amazon tool can manage terabytes of data every hour and can connect to hundreds of Data Sources using the Kinesis Data Firehose. This allows it to load data to AWS storage and Data Lakes with high throughput. IT logs, tracking data, etc are some of the information that is easiest to ingest using Amazon Kinesis.

Limitations of Cloud Data Ingestion

Cloud Data Ingestion is an important aspect of any Data Pipeline and provides numerous benefits to data professionals. However, it also comes along with the following limitations: 

• Building or even modifying a Data Ingestion Pipeline requires huge investments in terms of time, money and resources. Moreover, creating a new Data Pipeline from scratch whenever a new data source or business need shows up, hampers the speed of all the teams that rely on pipeline data.
• Applying changes to a Data Pipeline requires a workload of a minimum of 10-20 hours for Data Engineers. Moreover, 90% of this time is spent on activities related to maintenance and only 10% is utilized for the actual ingestion tasks. This makes the task of updating a Cloud Data Ingestion Pipeline hectic.
• Data Ingestion requires Data Teams to perform similar steps again and again. It also needs heavy troubleshooting & debugging which consumes a lot of time and effort. Due to this, Data Engineers do not get much time to innovate the technology and methods involved in Cloud Data Ingestion.

Use cases of Data Ingestion

• Real-Time Analytics: Streaming in data from IoT devices, social media platforms, or sensor networks into a data processing system in real time for required insights related to stock prices or activity monitoring by users.
• Data Migration: Transferring massive volumes of data from legacy systems to modern cloud platforms for more scalability and storage.
• ETL Pipelines: These are integrated processes of data extraction, transformation, and loading from diverse sources into a data warehouse, enabling enterprises to conduct full-scale analytics on their data.
• Customer 360 View: Data from every customer touchpoint-for instance, CRM systems and engagement through a website-is ingested to create one single version of a customer profile for personalized marketing and service.

Conclusion

This article introduced you to Cloud Data Ingestion and discussed its key features. It also explained the concept of Data Ingestion and elaborated on its importance. The article further discussed the parameters that are crucial for Data Ingestion and listed 5 popular tools to facilitate the ingestion process.

Furthermore, the article explained certain limitations associated with the Cloud Data Ingestion process and also provided a comparison between Data integration and Data Ingestion.

Now, setting up a Data Ingestion Pipeline is necessary for data-driven businesses. However, building an in-house solution for this process could be an expensive and time-consuming task. Hevo Data, on the other hand, offers a No-code Data Pipeline that can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Customer Management, etc.

This platform allows you to transfer data from 150+ sources to Cloud-based Data Warehouses like Snowflake, Google BigQuery, Amazon Redshift, etc. It will provide you with a hassle-free experience and make your work life much easier.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. Also checkout our unbeatable pricing to choose the best plan for your organization.

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