Data engineering is bringing unprecedented speed and accuracy and speed to business intelligence. Add visualization to this combo and you have the next-gen solution for a successful business intelligence project. Let’s take a look at how this trio is transforming the BI landscape.
Understanding data engineering
Data engineering is all about adding speed and accuracy to the data collation and transformation process before it is analyzed. It combines data science, machine learning ETL and other technologies to cleanse and prepare the data for analytics.
Most businesses spend considerable time and effort in finding where the data is, collating it and making it ready for analysis. As much as 50% time of an enterprise business intelligence project is spent on this activity. Mostly enterprise data is scattered across legacy systems, local machines, excel files, printed reports, etc. It is a task in itself to identify the relevant data from this pile, bring it all together and then cleanse it for analytics.
Data engineering takes care of data extraction, cleansing, integration, quality, and governance of data. Not just this, data engineering also enables businesses to identify which data is crucial for analytics. It enables them to collate, sort and categorize their structured or unstructured data and prepare it for further transformation. Careful choice of technologies, as well as the right human skills, are needed to perform these data engineering tasks correctly.
Data engineering processes
Data engineering helps business to build robust data infrastructures that can scale and support their digital Initiatives. Covering data preparation, ETL, and enterprise data warehousing, data engineering helps in building and automating smart data pipelines to bring more value to all data-driven processes. Core data engineering steps include:
ETL
collate data from disparate data sources. In simple steps, you can collect and combine data coming from your legacy data stores, ERPs, CRMs, RDBMS, flat files, big data lakes and warehouses, cloud data warehouses, streaming data sources, and more.
Continually ingest data into popular data warehouses or data sources in real-time with the advanced load step. Automate real-time data ingestion into your data stores or data warehouse platforms on-premises or on the cloud. With multiple actions, ingest data the way you want. Empower your users with a single, go-to data source to accelerate data-driven analytics and decisioning.
With simple drag and drop actions, move, cleanse, and transform your data with advanced steps. Define lean and scalable data architectures. Apply various functions like join and union, sort, filter, and advanced steps like formula fields, dynamic calculations, data science algorithms, GIS lookup, to efficiently transform your data in the shortest time.
Automation: Automate data workflows covering data extraction, transformation, and data loading. Create schedules for data fetch and ingestion and ensure that data pipelines have the most current data at any point in time. Automate running of data science algorithms to create data models and define triggers for the next actions basis the outcomes in each cycle. Ensure automated delivery of reports to desired recipients via specific channels, at regular time intervals.
Data Science
easy access to machine learning tools, predictions for your business and perform what-if analysis by yourself. Bring out unknown causative factors and analyse their effect on your business’ future in simple steps. Get trendlines on your business’ growth. Visualize the predictions and other outcomes in personalized views.
Inbuilt Machine Learning Actions, Deeper Insights
Apply statistical computing to derive patterns from your historical and live data.
Data Warehousing
Create seamless connections to your data warehouse platforms. Transform data collected in data marts and data lakes and automate pushing it to your data warehouse platform. Automate and fire direct queries in real-time and accelerate the availability of analytics-ready data. Manage data warehouses on-premises or in the cloud.
Empower data engineers with agility to create robust data workflows and add new data sources. Enable data scientists to access rich variety of data for machine learning and related tasks. Perform powerful ETL and automate real-time data ingestion. Intellicus connects to all popular data warehouse platforms like Snowflake, Amazon Redshift, Google BigQuery, Teradata etc.
