| To tackle this, enterprises need systems that enable real-time analysis, aggregation, and transformation of incoming data streams. Enterprises can harness the power of continuous information flow by lessening the gap between traditional architecture and dynamic data streams. Unstructured data formatting issues Increasing data volume gets more challenging because it has large volumes of unstructured data. In Web 2.0, user-generated data across social platforms exploded in the form of audio, video, images, and others. Unstructured data is challenging because it lacks a predefined format and doesnât have a consistent schema or searchable attributes. Like structured data sets that are stored in the database, these donât have searchable attributes. This makes it complicated to categorize, index, and extract relevant information. The unpredictable varying data types often have irrelevant content and noise attached to them. These require synthetic data generation, natural language processing, image recognition, and ML techniques for meaningful analysis. The complexity doesnât end here. It is difficult to scale storage and process infrastructure to manage the sheer increase in the volume. However, various advanced tools have been impressive in extracting valuable insights from the chaos. MonkeyLearn, for example, implements ML algorithms for finding patterns. K2view uses its patented entity-based synthetic data generation approach. Likewise, Cogito uses Natural Language Processing to deliver valuable insights. The future of data intergration Data integration quickly dissociates from traditional ETL (Extract-Transform-Load) to automated ELT, cloud-based integration, and others implementing ML. The ELT shifts the Transformation phase to the end of the pipeline, loading raw data sets directly into the warehouse, lake, or lakehouse. This enables the system to examine the data before transforming and altering it. The approach is efficient in processing high-volume data for analytics and BI. A cloud-based data integration solution called Skyvia is pioneering the space and enabling more businesses to merge data from multiple sources and further them to a cloud-based data warehouse. Not only does it support real-time data processing, but also greatly improves operational efficiency. The batch integration solution covers legacy and new updates, and is easily scalable for large data volumes. It fits perfectly well for consolidating data in the warehouse, CSV export/import, cloud-to-cloud migration, and others. Since 90% of data-driven businesses could be inclined towards cloud-based integration, many popular data products are already ahead in the game. Further, in times to come, businesses can expect their data integration solution to process virtually any kind of data without compromising operational efficiency. That means data solutions should soon support advanced elastic processing that can work on multiple terabytes of data in parallel. Next, serverless data integration will also get popular as data scientists look forward to nullifying the effort needed to maintain the cloud instances. Stepping stones to a data-driven future In this post, we discussed the challenges from disparate data sources, divide-driven streaming data, unstructured formats, and others. Enterprises should act now and implement careful planning, advanced tools, and best practices to achieve seamless integration. At the same time, it is worth noting that these challenges are potential opportunities for growth and innovation if worked upon in time. By taking up these challenges head-on, enterprises can not only utilize the data feeds optimally but will also inform their decision-making. | 