5 Proven Steps to Follow for Successful Big Data Integration
Businesses nowadays generate considerable amounts of data in their day-to-day operations and processes. An average company uses dozens of applications and several other on-premise systems for storing enterprise data.
Of course, while businesses are creating loads of data every second, the data is no good if it is not adequately analyzed and integrated. Loads of data can get piled up, and it will ultimately become unmanageable.
This is where big data integration comes into play, as it can help you obtain the right information and use it effectively to grow your business while complying with data privacy regulations.
What is (big) data integration?
Data and big data integration refer to sets of processes that are used to gather and combine data from disparate sources into meaningful and valuable information. A complete data integration solution delivers veritable data from a wide range of sources.
It is worth noting that traditional data integration methods were mainly based on the ETL (extract, transform, and load) process to ingest and clean data that would then be loaded into a data warehouse.
But today, giant volumes of data are gathered from many different data sources which are generating data in real-time with different qualities (which is called big data). Big data integration is very challenging, especially after the traditional data integration methods fail to handle it.
How is big data integration different from traditional data integration?
It is safe to say that big data integration differs from traditional data integration in many ways and the following are just the main characteristics of big data.
Volume
This is the original attribute of big data. Today, the number of connected devices and people is much higher than before, which highly influenced the number of data sources and the amount of data.
Velocity
Data sources are increasing, and the rate of data generation is increasing as well. This is especially true after the appearance of social media and the use of IoT.
Variety
An increasing number of data sources imply that there is a wider variety in the formats in which data is stored.
Veracity
Everything listed above causes a situation where we have different data quality. Thus, we can find uncertain or imprecise data.That’s why it’s crucial to have a strategy in place that ensures effective big data integration.
1. Use the right data integration tools
It’s fair to say that there are many tools used in big data integration. Some of them were used in traditional integration processes and were improved to fit the big data needs. That is how a traditional tool evolves into a tool that can satisfy big data needs. They can be categorized as commercial and open-source.Here are some of the more prominent data integration tools:
- Spark
- MapReduce
- Powercenter
- Pig
- Open Studio (also used for traditional data)
2. Use a good combination of traditional practices with good data ingestion methods
First of all, it is worth noting that the traditional ETL approach usually runs overnight, with data refreshing on a 24-hour cycle.
Even though this type of data integration is still effective to an extent, much of today’s data needs to be handled differently. Think about machine information from manufacturing plant machines, sensors on vehicles, as well as handheld gadgets. This information may stream ceaselessly or at indicated intervals.
Bear in mind that as you work toward data integration modernization, you will also need to be able to capture and process new types of data at different speeds and with different tools. This requires either new DI solutions or adjustments to older techniques.
When you capture fresh data in its original state, you can repurpose it to make it ready for reporting, analytics, and operations. Luckily, the modern quick, versatile equipment and programming make it functional to manage information at whatever speed is required.
3. Say yes to new data prep practices and tools
Data preparation has many names. Some refer to it as data wrangling, some call it data munging, and some even refer to it as data blending.
Call it what you want, but there are many tools that can help you prepare data for other uses – tools for integration, profiling, quality, exploration, analytics, and visualization. Data preparation for analytics is a common method that data scientists and analysts use when working with source data.
Consequently, it’s a vital part of your toolkit for data integration modernization. Data analysts and scientists are determined to find important chunks of insight covered up in the information, for example, new customer segments indicated by outliers.
New data prep tools and methods can help because they are fast, flexible, and easy to use. Additionally, they encourage data exploration on the go.
4. Use DI modernization to allow self-service access
Numerous individuals depend on self-service capacities, data prep and access, visualization, report creation, and also analytics.
These can be both regular users and data scientists.
Self-service capabilities allow freedom and spontaneity and are beneficial for both business and IT users.
Data integration modernization approaches should improve self-service data access and prep through the practices below:
- Incorporating information explicitly for self-service, for example, by feeding big data into vaults, data lakes, and enterprise data hubs.
- Using self-service tools to introduce data views that are business-friendly, as well as to allow for self-service preparation and access.
5. Add more right-time functions to the data integration modernization solutions
Today, efficiency is the name of the game, and an efficient approach is what you need when it comes to data integration. Choose an approach that allows users to work in real-time as well as at other speeds and frequencies required for specific business processes and databases.
Improving data integration to happen at the right time requires methods like high performance, micro-batch, and data federation. Luckily, modern data integration platforms are multitool environments that can handle data at the appropriate speed and frequency.
Final words
Having read this article, you are familiar with what traditional DI brought to users and how big data transformed this technology.It is now your turn to take this to your advantage and improve every process that deals with data at your office. Also, try to use the tools you have seen here to make the whole experience even smoother.