As digital transformation has picked up steam, big data has transpired as the essential fuel for the journey.
It’s a good news-bad news scenario.
On one hand, large files or big data is gold. It’s of tremendous value. When processed properly, massive and unprecedented torrents of data can yield nuggets of insightful information that underpins digital transformation efforts.
But on the other hand, many organizations find themselves drowning in the tidal wave of data. They are unable to use troves of data to their benefit, which impacts their digital transformation initiatives.
While many put the onus on the data analytics, the storage remains overlooked. The key to surviving the big data deluge is to implement a data ingestion strategy that captures all the available data to generate value.
Ingestion of Big Data
Data ingestion is the process of movement of data from disparate sources (in-house apps, databases, spreadsheets, etc.) to a reservoir or data lake or data warehouse where it can be accessed, used, and analyzed. Based on the source and destination of the data, large volumes of data can be digested in real-time, batches, or both. When data is ingested in real-time, it is sourced, manipulated, and loaded as soon as it’s created or recognized by the data ingestion layer. On the flip side, when data is ingested in batches, data is imported into distinct groups at regular intervals of time.
In many situations, the source and destination do not have the same format, protocol, or type. – a fact that amplifies the challenge of data ingestion.
In such situations, traditional business intelligence and data warehouse techniques will not be of any help. What companies need is a next-gen data ingestion platform that provides the opportunity to store vast amounts of data without compromising quality or speed.
Modern big data ingestion tools can ingest data from disparate sources, in various formats, into a data lake without heavy coding and additional infrastructure. Here the data cannot only be cleansed from errors but also analyzed for making decisions. The information can be used to drive big data initiatives and meet business’s present as well as future requirements.
Despite the benefits, doing away with all the challenges of big data ingestion is not easy. Here are a few challenges companies need to handle as they aim to ingest big data.
Information can come from distinct sources, external and internal both. From RDBMS data to REST APIs, this can include anything. These sources are evolving while new ones come to light, making data ingestion process burdensome and time-consuming. IT teams have to spend a lot of time to ingest such voluminous information and later integrate it into a unified database. Due to delays in the data ingestion and integration, decision-making speed hampers – delivering poor customer value and making organizations difficult to do business with.
Legal along with compliance requirements add complexity to the data pipelines. Say, for example, US healthcare data has to comply with HIPAA i.e. Health Insurance Portability and Accountability Act, European nations have to comply with GDPR i.e. General Data Protection Regulation, and companies using third-party IT services have to comply with SOC 2 i.e. Service Organizational Control 2.
Businesses ingest and integrate data to extract invaluable insights that help them make important decisions. Process and pipeline challenges faced during the ingestion process can impact every stage down the line.
Successful organizations Use a Self-Service Approach
Self-service powered data integration solutions can empower businesses users curb these challenges and make most of big data ingestion and integration processes.
With features such as pre-built application connectors, shared templates, user-friendly dashboards, etc, users can onboard, ingest, and integrate disparate data gathered from disparate sources in no time. No matter how complex or different sources are, data can be ingested easily. Self-service integration solutions also meet the legal and compliance requirements of data.
Apart from that, self-service powered tools empower users ingest large data streams and then integrate without IT support. As a result, IT teams no longer have to spend hours coding and handling different data types to ingest and integrate data. They can monitor the operations for authenticity. In short, users can perform data ingestion and data integration in minutes while allowing IT to take up the governance role.