This is the last of our three-part series on the Future of Data Integration technologies. In our previous article, we discussed the rise of real-time ETL and Data Warehousing and the active part it has played in revolutionizing Business Intelligence. In this article, we will discuss why ETL is giving way to new Data Integration Technologies.
Though ETL has been extremely useful and efficient in handlining the increasing volumes of data, the continued onslaught poses a problem. While many enterprises have turned to real-time ETL and Data Warehousing, it is not always a feasible solution. Earlier data migration projects relied on ETL to read data from a database, transform it using lookup tables and rules and load it to a centralized warehouse. However, since data has breached the boundaries of enterprise-based data warehouses, spilling over into cloud applications and storages, ETL’s limitations are glaringly visible. Software service providers are hard-pressed to perform speedy, yet cost-effective data integrations in near real time. Therefore, enterprises are looking at superior alternatives to replace ETL. Here are the key reasons for the decline of ETL and the emergence of new data integration technologies.
- The Rise of Data Lakes – With analytical warehouses outliving their usefulness and data volumes growing by the day, most enterprises are evaluating technologies which offer the flexibility and agility of deploying data anytime and anyhow without using multiple integrator tools and platforms. Targeting these companies, the new breed of data integration and migration services providers are offering the simplicity of a data lake which makes it possible to quickly move small and large data storages into a single large pool without resorting to multiple integration platforms.
- The popularity of Data Services – While ETL and data warehouses are still widely used for data integration and migrations, the increasing complexity of business data, the use of cloud applications and resources, the proliferation of devices and multiple analytics and reporting requirements are forcing companies to turn to new data management and governance practices. Data services are gaining popularity for their flexibility and data integration is evolving to provide quick access to structured and unstructured data while cloud data services offer the scalability required for achieving business goals.
- Increasing frequency of Data Migrations – The dynamic nature of data today means that at any given time, most companies are involved in some data migration projects to keep up with ongoing business changes and system upgrades. These migrations must be quick and seamless in order to respond quickly to changes. Hence the evolution of data integrations and migrations have taken a new path. They no longer require burdensome IT initiatives to generate quick and actionable insights. Most migration projects today boast simple, straightforward processes, reasonable costs, and quick turnaround times.
- The emergence of SMAC data – Data is no longer restricted to warehouses and applications. Today, data from social platforms, cloud applications, and mobile devices are also taken into account for generating analytics and business insights. Traditional ETL, however, isn’t made to enable access this self-service data; this explains the growing popularity of SMAC (social-mobile-analytics-cloud) and the kind of agile data integration/migration services that can extract and transform this data into valuable, strategic business insights.
The Future of Data Integration Technologies
It is, therefore, evident that the need of the hour is a shift in paradigm, a new way of looking at data and new tools and data integration technology to effectively utilize that data.
Enterprises are therefore exploring new data integration technologies where a self-service, real-time integration platform updates data in near real-time thereby freeing up capital and resources to concentrate on generating valuable insights for strategic decision making.
CyByte’s Data Integration Solutions can help you integrate your cloud and on-premise applications, structured and unstructured data in near-real time with customized connectors to translate raw data into actionable insights.