At Integral and Open Systems we design, build, and maintain data pipelines, data warehouses and integrate specialization around the operation of big data and distributed systems, along with concepts around the extended Hadoop ecosystem, stream processing, and in computation at scale. Data wrangling is a significant problem when working with big data, especially if you haven’t been trained to do it, or you don’t have the right tools to clean and validate data in an effective and efficient way
We make sure the data the customer is using is clean, reliable, and prepped for whatever use cases may present themselves. We wrangle data into a state that can then have queries run against it by data scientists.
We design and build high performing custom ETL pipelines and operate widely available pipelines from cloud services to serve your needs.
Extract
Extract: This is the step where data = land (e.g. a upstream source could be machine or user-generated logs, relational database copy, external dataset … etc). Upon available, we transport the data from their source locations to further transformations.
Transform
Transform: We apply algorithms and perform actions such as filtering, grouping, and aggregation to convert raw data into analysis-ready datasets. This step requires significant amount of business understanding and domain knowledge.
Load
Load: Finally, we load the processed data and transport them to a final destination. Often, this dataset can be either consumed directly by end-users or it can be treated as yet another upstream dependency to another ETL job, forming the data lineage.
We have significant amount of experience in Airflow, Luigi and Pinball to help resolve our customers problems more elegantly.
We can help you deal with the following functions
- Moving data to the cloud or to a data warehouse
- Wrangling the data into a single location for convenience in machine learning projects
- Integrating data from various connected devices and systems in IoT
- Copying databases into a cloud data warehouse
- Bringing data to one place in BI for informed business decisions
Besides big data capabilities, data lakes also brought new challenges for governance and security, and the risk of turning into a data swamp – a collection of all kinds of data that is neither governable nor usable. To tackle these problems we create datahub – where data is physically moved and re-indexed into a new system.
In a data hub, data from many sources is acquired through replication and/or publish-and-subscribe interfaces. As data changes occur, replication uses changed data capture to continuously populate the hub, while publish-and-subscribe allows the hub to subscribe to messages published by data sources. The data-centric storage architecture enables executing applications where the data resides.
Here are the benefits of this approach
- Easy connection of new data sources. Data hub can connect multiple systems on the fly, integrating the diverse data types.
- Up-to-date data. Outdated data can be an issue but the Data hub overcomes it, presenting fresh data ready for analysis right after capturing it.
- Rapid deployment. Our Data-hub deployment is a matter of days or weeks.
