What Is Data Ingestion Process

data ingestion

Organizations today rely heavily on data for predicting trends, forecasting the market, planning for future requirements, understanding consumers, and business decision-making. But to accomplish these tasks, it is essential to get fast access to enterprise data in one place. This is where data ingestion comes handy. It enables extraction of information from disparate sources so that you can uncover the insights concealed in your data and use them for business advantage.

Data ingestion is the transportation of data from assorted sources to a storage medium where it can be accessed, used, and analyzed by an organization. The destination is typically a data warehouse, data mart, database, or a document store.


Data ingestion can be performed in different ways, such as in real-time, batches, or a combination of both (known as lambda architecture) depending on the business requirements.

  • Real-Time Data Ingestion

Data ingestion in real-time, also known as streaming data, is helpful when the data collected is extremely time sensitive. Data is extracted, processed, and stored as soon as it is generated for real-time decision-making. For example, data acquired from a power grid has to be supervised continuously to ensure power availability.

  • Batch Data Ingestion

When ingestion occurs in batches, the data is moved at recurrently scheduled intervals. This approach is beneficial for repeatable processes. For instance, reports that have to be generated every day.

  • Lambda Architecture

The lambda architecture balances the advantages of the above mentioned two methods by utilizing batch processing to offer broad views of batch data. Plus, it uses real-time processing to provide views of time-sensitive information.

What is data ingestion pipeline?

data ingestion pipeline moves streaming data and batched data from pre-existing databases and data warehouses to a data lake. Businesses with big data configure their data ingestion pipelines to structure their data, enabling querying using SQL-like language.


  • Apache NiFi (a.k.a. Hortonworks DataFlow)
    Both Apache NiFi and StreamSets Data Collector (detailed below) are Apache-licensed open-source tools. Hortonworks offers a commercially supported variant, Hortonworks DataFlow (HDF).NiFi processors are file-oriented and schema-less. This means that a piece of data is represented by a FlowFile (this could be an actual file on disk, or some blob of data acquired elsewhere). Each processor is responsible for understanding the content of the data in order to operate on it. So if one processor understands format A and another only understands format B, you may need to perform a data format conversion between those two processors.NiFi has been around for about the last 10 years (but less than 2 years in the open source community). It can be run standalone, or as a cluster using its own built-in clustering system.
  • StreamSets Data Collector (SDC)
    StreamSets takes a record-based approach. As data enters your pipeline (whether it’s JSON, CSV, etc.) it is parsed into a common format. This means that the responsibility of understanding the data format is no longer placed on each individual processor, and so any processor can be connected to any other processor. SDC also offers great flexibility. It runs standalone and as a clustered mode, running atop Spark on YARN/Mesos, leveraging existing cluster resources you may have.StreamSets was released to the open source community in 2015. It is vendor agnostic, and Hortonworks, Cloudera, and MapR are all supported.
  • Gobblin
    Gobblin is an ingestion framework/toolset developed by LinkedIn. It is open source. Gobblin is a flexible framework that ingests data into Hadoop from different sources such as databases, rest APIs, FTP/SFTP servers, filers, etc. It is an extensible framework that handles ETL and job scheduling equally well. Gobblin can run in standalone mode or in distributed mode on the cluster.
  • Sqoop
    A common ingestion tool that is used to import data into Hadoop from any RDBMS. Sqoop provides an extensible Java-based framework that can be used to develop new Sqoop drivers to be used for importing data into Hadoop. Sqoop runs on a MapReduce framework on Hadoop, and can also be used to export data from Hadoop to relational databases.
  • Flume
    A Java-based ingestion tool, Flume is used when input data streams-in faster than it can be consumed. Typically Flume is used to ingest streaming data into HDFS or Kafka topics, where it can act as a Kafka producer. Multiple Flume agents can also be used collect data from multiple sources into a Flume collector.
  • Kafka
    Kafka is a highly scalable messaging system that efficiently stores messages on disk partitions in a Kafka topic. Producers publish messages as Kakfa topics, and Kafka consumers consume them as they please.

Data Ingestion Benefits

Data ingestion has numerous benefits for any organization as it enables a business to make better decisions, deliver improved customer service, and create superior products. In other words, the process helps a business gain a better understanding of its audience’s needs and behavior and stay competitive. Counting on data ingestion is one of the most effective ways to deal with inaccurate, unreliable data.

Challenges Associated with Data Ingestion

The following are the key challenges that can impact data ingestion and pipeline performances:

  • Sluggish Processes

Writing codes to ingest data and manually creating mappings for extracting,cleaning, and loading data can be cumbersome as data today has grown in volume and become highly diversified.

Therefore, there is a move towards data ingestion automation. The old procedures of ingesting data are not fast enough to persevere with the volume and range of varying data sources.

  • Increased Complexity

With the constant evolution of new data sources and internet devices, businesses find it challenging to perform data integration to extract value from their data.

This is mainly because of the ability to connect to that data source and cleaning the data acquire from it, like identifying and eliminating faults and schema inconsistencies in data.

  • The Cost Factor

Data ingestion can become expensive because of several factors. For example, the infrastructure you need to support the various data sources and patented tools can be very costly to maintain in the long run.

Similarly, retaining a team of data scientists and other specialists to support the ingestion pipeline is also expensive. Plus, you also have the probability of losing money when you can’t make business intelligence decisions quickly.

  • The Risk to Data Security

Security is the biggest challenge that you might face when moving data from one point to another. This is because data is often staged in numerous phases throughout the ingestion process. This makes it challenging to fulfill compliance standards during ingestion.

  • Unreliability

Incorrectly ingesting data can result in unreliable connectivity. This can disrupt communication and cause loss of data.

Data Ingestion Best Practices

To protect your data from the challenges discussed above, we’ve compiled three best practices to simplify the process:

Anticipate Difficulties and Plan Accordingly

The prerequisite of analyzing data is transforming into a useable form. As the data volume increases, this part of their job becomes more complicated. Therefore, anticipating the difficulties in the project is essential to its successful completion.

So, the first step of data strategy would be to outline the challenges associated with your specific use case difficulties and plan for them accordingly. For instance, identify the source systems at your disposal and ensure you know how to extract data from these sources. Alternatively, you can acquire external expertise or use a code-free data ingestion tool to help with the process.

Automate the Process

As the data is growing both in volume and complexity, you can no longer rely on manual techniques to curate such a huge amount of data. Therefore, consider automating the entire process to save time, increase productivity, and reduce manual efforts.

For instance, you want to extract data from a delimited file stored in a folder, cleanse it, and transfer it into the SQL Server. This process has to be repeated every time a new file is dropped in the folder. Using a tool that can automate the process by using event-based triggers can optimize the entire ingestion cycle.

In addition, automation offers the additional benefits of architectural consistency, consolidated management, safety, and error management. All this eventually helps in decreasing the data processing time.

Enable Self-Service Data Ingestion

Your business might need several new data sources to be ingested weekly. And if your company works on a centralized level, it can face trouble in executing every request. Therefore, making the ingestion process automated or opting for self-service data ingestion can empower business users to handle the process with minimal intervention from the IT team.


Data ingestion tools can help with business decision-making and improving business intelligence. It reduces the complexity of bringing data from multiple sources together and allows you to work with various data types and schema.

Moreover, an efficient data ingestion process can provide actionable insights from data in a straightforward and well-organized method. Practices like automation, self-service data ingestion, and anticipating difficulties can enhance your data ingestion process by making it seamless, fast, dynamic, and error-free.

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Author: refuge_2020

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