The data ingestion process is essential for companies that use data regularly.
Most companies benefit from ingesting data because of its effect on the product and customers (as you’ll read throughout this post). Despite its importance, however, data ingestion doesn’t come without its challenges. Fortunately, there are tools that can alleviate some of these challenges.
Let’s dive in.
What is data ingestion?
Data ingestion is the process in which data is transported from one location to another.
Once in its final location, data can be processed and analyzed. The procedure of data ingestion can also be broken down into different types.
One type of data ingestion is streaming the data; another is batch processing. Companies will stream their data to make it available in real-time. Batch ingestion, as the name implies, retrieves data in scheduled or manual batches.
The best type of ingestion process is dependent on your company’s needs.
For example, real-time streaming ingestion would be useful if you use IoT sensors, medical data, advertising data, app click-stream data, or fraud detection.
Batch processing is the opposite—where time is not of the essence and acquiring a larger, historical dataset is more important. A use case for batch processing would be performing ad hoc analysis of trends like past customer purchases or your internal payroll structure.
As you can see, these batch processes don’t need data to be ingested by being streamed live.
Whether it’s streaming ingestion or batch ingestion, data ingestion is important because companies rely on their data—from customer behavior analysis to machine learning algorithms.
Where does data ingestion fit into the data stack?
Data ingestion happens at the beginning of your data stack. You retrieve data from various sources and ingest it for it to then be warehoused and transformed. From there on, you query and build visualizations from that data.
The data stack can be summarized as: discovering and acquiring data sources, ingesting your data, warehousing and transforming it (the order of which depends on whether you use ELT or ETL), visualizing and analysis, and, finally, syncing and automating your data and data-driven decisions.
Having a proper data ingestion process (and full data stack) in place is critical. However, there are some challenges you should anticipate and prepare for, even before ingestion occurs.
Data Ingestion Challenges
Below are three data ingestion challenges that you can expect to experience if you don’t have the right tools on hand.
1. Slow Data Processes
Your data likely comes from multiple sources, which results in data exported in various formats. To properly move your data through the data stack, data transformation may take more time than anticipated.
These factors can lead to slow data ingestion.
As you can expect, a slower process can cost more money. Data ingestion may also require additional resources like data engineers or database developers, as well as platforms or tools to maintain those processes.
Your company may also use an older process tha’s costly to maintain. These parts of ingesting data can add up to an expensive investment.
3. Compromised Data Security
The definition of data ingestion is to transport data from one spot into another location—this can open up vulnerabilities that can compromise the data.
If you rush to ingest data, your company may not have all the necessary checks and balances in place to ensure data security. This can lead to unecessary breaches and security challenges.
Data Ingestion and Integration Tools
Fortunately, data ingestion challenges can be solved by a variety of processes and tools.
Firstly, the ingestion process can be automated, which reduces costly resources like hiring dedicated data teams.
By automating your data ingestion, everyone can access, prepare, and act on the data they need as well as link their data to their favorite spreadsheet tool.
Some ingestion tools can expedite the data ingestion process, plus securely connect and query your data. These include Apache Kafka, Amazon Kinesis, and Fluentd.
You may also use platforms in your organizaiton to accessa nd organizaiton your data—Kloudio can combine and integrate your suite of tools to craft a seamless, automated data stack, including:
Spreadsheet Integration and Data Reporting Tools for an Integrated View of Data
For business users who don’t want to rely on code for data integration, you may utilize platforms like Microsoft Excel, Google Sheets, and Airtable.
Data Integration Tools for SQL Databases and Data Warehouses
If you are a data engineer or data analyst, you may be more familiar with platforms like PostgreSQL, Amazon S3, and Oracle that can update without any technical data integration knowledge.
Web Application and Cloud Data Integration
Combine tools like Salesforce, Oracle Cloud, and Google Ads with your Kloudio account. The tool offers an intuitive report builder that gives you clever view of your customer data and integrates your disparate sources with cloud data integration.
Over to You
Data ingestion can be complex, inefficient, insecure, and expensive—if you let it. With Kloudio, it doesn’t have to be. Utilizing Kloudio’s data integration tools can help you and your company to properly ingest your important data.
Discover how to craft a modern data stack that automates your data ingestion and more.