Big data has transpired to be the fuel for organizations’ digital transformation journey. It has been of tremendous value indeed. These large files, both structured and unstructured, when processed properly, generate nuggets of insightful information that underpins digital transformation efforts. Nevertheless, many organizations find themselves drowning in the tidal wave of data. They find it difficult to capitalize on troves of data, which, in turn, impacts their digital transformation initiatives.
Though there is a school of thought that puts the onus on the data analytics, it overlooks storage part completely. The key to handling big data is to implement a data ingestion strategy that captures and absorbs all the available data to generate value.
All about Big Data Ingestion Platform
The process of data movement from disparate sources (databases, in-house apps, spreadsheets, etc.) to a data lake is called data ingestion. Also known as data lake ingestion, this process allows users to access, use, and analyze data.
The ingestion can happen either in real-time, batches, or both. In real-time ingestion, data is sourced, manipulated, and loaded as soon as it’s created by the data ingestion layer. In batch ingestion, data is imported into different groups at regular intervals of time.
In a lot of cases, the source and destination do not have the same format, type, protocol etc. This amplifies the challenge of data ingestion. In these cases, technologies such as business intelligence and data warehousing will be of little help. Modern data lake ingestion platforms are of vital value here. It provides the opportunity to ingest and store different types of data without compromising quality or speed.
Next-gen big data ingestion platforms ingest data from assorted sources (wherein data is present in different formats), into a data lake at the speed of business. These tools allow users ingest data without seeking IT intervention. Not only data can be cleansed from errors but also analyzed for making decisions. The information can be used to kick start big data initiatives without a hitch. Even though big data ingestion offers benefits, handling challenges related to it is not easy. Here are a few challenges enterprises must handle in order to ingest big data.
- The biggest challenge when it comes to ingesting data is that information can come from different sources, external and internal both. This can include anything, be RDBMS or REST APIs. Such different sources make data ingestion process burdensome and time-consuming. Technical teams have to invest a lot of time and effort to integrate data into a unified database. Owing to delays, the decision-making process gets impacted – compromising customer value and making organizations difficult to do business with.
- Another challenge that arrives during data ingestion is the degree of legal as well as requirements required. For example European nations must comply with GDPR, US healthcare data act must comply with HIPAA, and companies relying on third-party IT services must comply with SOC 2.
These challenges must be dealt with proactively in order to capture the true potential of data. And, modern solutions that use a self-service approach can play a significant role in helping companies ingest data while overcoming these challenges.
A Self-Service Approach Does It All
Self-service powered solutions enable business users ingest data without seeking technical or IT support. These tools also allow users deal with the challenges mentioned with ease and speed. Ergo, self-service solutions can empower business users handle these challenges and ingest datasets without excessive IT support.
With features such as pre-built application connectors, monitoring dashboards, shared templates, etc. self-service ingestion solutions can onboard, ingest, and integrate data gathered from different sources with speed. They can ingest data from complex sources without difficulty. In addition, they have the power to meet the legal and compliance requirements of data.
As business users don’t need to rely on IT excessively, IT becomes free to focus on the governance role. This makes IT highly productive. IT teams no longer need to invest hours in coding and handling different data types to ingest and integrate data. They need to simply govern and control the operations, driving innovation and growth. Thus, self-service solutions empower users ingest data and integrate data in minutes while making IT free to focus on governance role.
