Gravity Data

Data Ingestion: Guide to Your Business’ Data Strategy

Data Ingestion: Guide to your Business’ Data Strategy

People say that data is one of a business’ most valuable assets. Why? Because businesses can analyse data to extract useful intelligence and insights that guide decision-making. This data that this analysis is based on comes from a range of sources, so it’s very important that it’s accurate.

If the picture is incomplete, misleading reports could be generated. You don’t want to make plans for your business based on partial or problematic data! That’s why data from multiple sources should be kept in one place. There are special types of databases designed for this purpose, and they’re called data warehouses.

Before information can be digested, it has to be ingested. Whether you’re an analyst, a manager, or a decision maker, you should understand how data ingestion occurs and the technologies it uses. If you can improve your data pipeline, this will bring value to your business.

What Is Data Ingestion?

The process of data ingestion involves transporting data from different sources to one storage medium. From this place, an organisation can then access it, use it, and analyse it as required. This destination is usually a data warehouse, database, or some kind of document store. The actual sources could vary; the data might include SaaS data, spreadsheets, or in-house apps, for example.

The data ingestion layer is a fundamental element of any architecture for analytics. This is because analytics systems need consistent, accessible data. There are a few different ways that data can be ingested, and the data ingestion layer could be designed based on one of various models.

What Are the Types Of Data Ingestion?

There are different ways to ingest data, but the most common is batch processing. This consists of an ingestion layer that collects and groups source data then sends it to the data warehouse or other destination system. The groups might be processed based on logical ordering, a schedule, or the activation of specific conditions. It’s a system that works when having almost real time data isn’t so important. It’s not as fast as streaming ingestion, but it’s easier and more affordable to implement.

What is streaming ingestion, you ask? Well, this is the real-time processing of data which doesn’t involve any grouping at all.  Instead of being grouped, it’s simply sourced then manipulated and loaded straight away! Of course, this requires your systems to be constantly monitoring sources to accept new information. But its immediacy makes it ideal for analytics that depend on up-to-the-second data.

Double-check the streaming platform that you use. Some of them operate on batch processing but the groups are smaller, which is why it’s sometimes called “micro batching.” Some people consider this another type of data ingestion.

The right data ingestion method for your business will depend on the data you’re ingesting and what you’re planning to use it for!

Common Challenges Faced By Data Ingestion

Process Challenges

The kinds of data available evolve and diversify every day, and the volume of data that businesses are ingesting has exploded! You might be dealing with data from SaaS platforms, mobile devices, or transactional databases – or maybe all three! That’s why it’s difficult to plan your ingestion process: you may find yourself required to update it at any time!

You’ll have to spend money to code and maintain appropriate analytics architecture that’s capable of ingesting diverse data in large volumes. In the end, it should be a good investment, though. After all, your competitive analysis will only be improved by access to more data.

Speed is another challenge, and it’s felt both at the ingestion stage and in the data pipeline. Complex data takes longer to be ingested in “real time.” Sometimes “real time” refers to applications that experience regular 10-minute updates and other times it’s a far more immediate affair – like a stock ticker that constantly updates during trading hours.

An important question to ask is “does my organisation really need real-time processing?” The answer will help you decide the right method of data ingestion for you.

Pipeline Challenges

Of course, there are legal requirements involved when it comes to data pipelines. In Europe, General Data Protection Regulation dictate correct practice. In the US, the Health Insurance Portability and Accountability Act covers healthcare data. If you’re utilizing third-party IT services, you’ll also need to adhere to auditing procedures.

Comprehensive planning is crucial to ensure a pipeline that performs to standard. After all, businesses use the data from their analytics to make crucial decisions. This data is only valuable if it can be ingested and integrated. Any problems at this stage will create further problems down the line. Its importance shouldn’t be underestimated!

The Role of Data Ingestion In ETL

There are now many new techniques to replicate data for analysis purposes. This is largely thanks to the rise of cloud-based storage solutions.

Data ingestion used to require a procedure called ETL, which included three steps: extracting, transforming, and loading. Until recently, data had to be taken from the source and altered to fit the specifications of the destination system or the business’ needs. Then it could be added to the system.

This made sense when analytics systems were in house and expensive because it meant that businesses were doing as much data preparation as possible before loading it into the storage structure or data warehouse.

These days, that’s no longer necessary. Amazon Redshift, Google BigQuery, and other cloud data warehouses are able to scale and store data in a much more cost-effective fashion. It frees up data engineers too: they no longer have to perform preload transformations. All they must do is load the raw data. Data scientists must define the transformations in SQL, running them into the data warehouse at the moment of integration. This is how ETL became ELT!

ELT isn’t just preferable because it’s faster. It’s also easier as there’s no need to write complicated transformations for the data pipeline. You also need less on-premises hardware. Thanks to ELT, data analytic teams have more freedom and flexibility: they can develop transformations that suit their specific needs.

How Gravity Can Streamline Your Data Ingestion

Do you want to optimize your data ingestion processes for the benefit of your overall business? You need Gravity Data. We’re a team of data engineers who can guide you through the world of data and help unlock its potential for your organisation. We’d be delighted to discuss your needs and help you find cost-effective, efficient solutions.