Cloud ETL Platform

ETL is the primary step involved in any form of data analytics effort applied at any scale. Every data science professional has to extract, transform, and load (ETL) data from different data sources.

Are you struggling with high volumes of data? Our Big Data specialists perform all the analytics activities required, such as cloud storage, and data warehousing activity.

We design data pipelines to enable a smooth flow of data from one stage to the next. We focus on eliminating and automating many of the manual processes involved in data extraction, transformation and loading. We create designs for businesses in order to reduce errors and avoid bottlenecks or latency issues. We use pipelines to load the data into data lakes.

Whether you are at the pre-MVP stage or have a working prototype, we can help. Let us enable you to define data product requirements and functionalities by creating solutions for problems like predictions, classification, segmentation and anomaly detection.

Extract-Transform-Load (ETL) vs Extract-Load-Transform

ETL and ELT are not new to data professionals. Both techniques describe transformations, either before loading or after loading the data. In ELT, the transformation happens on the target, whereas with ETL, it may happen on the source side or on a compute layer in-between. If you are processing Big Data, you may want to use ETL followed by an Azure Data bricks Spark cluster to transform the data in an optimised environment with lower latency.

 If you have the same source and destination, you can leverage the compute power of a system like Azure SQL Data Warehouse to transform data. Since ELT takes place in the first stage of loading raw data on the target, you can leverage the power of a system like Azure SQL Data Warehouse to transform the data parallel. As there is no right or wrong way to process data, you may want to look at various parameters such as the data source and destination, latency, scalability, performance, cost, skill set, and so on, to decide on whether to use ETL or ELT.

ETL with Hadoop

Given that Hadoop-based Map Reduce programming is a relatively new skill, we have highly knowledgeable staff that specialise in empowering organisations to implement and manage this data flood. ETL tools, like Pentaho and Talend, offer a visual, component-based method to create Map Reduce jobs, allowing ETL chains to be created and manipulated as visual objects. Such tools offer a simpler and quicker way for staff to approach Map Reduce programming. At this entry point, they offer a great deal of pre-defined functionality that may be merged so that complex ETL chains can be created and scheduled. 

ETL-Data Migration

Our experienced team will pinpoint the real value in your data.

Digital Notation helps businesses with their data of diverse characteristics – such as variety (structured, unstructured, semi-structured), timing (real time, streaming, offline), location (on-premise & cloud) and volume.

Our experts bring demonstrable expertise into defining and executing the right strategy for data management across the above characteristics and tool sets such as Azure Data Factory, Apache Nifi, SAP SDI, SAP Data Hub, SAP Data Services and more.

ETL Tools

We enhance data capabilities to drive greater results and accelerate decision making with ETL using tools such as Spark, Hadoop, Hive, Azure, AWS, Snowflake, Google Big Query, Talend and many more.

This way, we are able to transform businesses by leveraging the power of data, helping enterprises gather and centralise actionable data so that they can make profitable and smarter business decisions with smooth operational capabilities. In-depth analysis and constant upgrading takes the guesswork out of business decisions.

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0208 124 3900

Liverpool Innovation Park, Digital Way, Innovation Boulevard, Liverpool. L7 9NJ