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How a Top National Retail Chain Uses Nexla to Make Data Analytics-ready
The retail chain operates more than 1000 physical stores and online stores and generates around $9 billion in sales annually, making it one of the top 50 national retailers in the US. Store level data and online customer data need to be processed, aggregated, and sliced daily in various ways to fulfill analytical tasks that drive successful operations and decision-making.
There is significant heterogeneity in data sources (FTP Servers, S3 buckets, REST/SOAP API, AS400, Facebook), formats (JSON, XML, CSV), ingestion speeds (batch/stream), domain implications, and destinations across business units. To make all data analytics-ready, the heterogeneity needs to be mitigated when migrating data to Google BigQuery. This put a significant strain on the data engineers at the company. Before using Nexla, migrating data from Mainframe systems and partner systems along the supply chain to Google BigQuery would take a few months of engineering time with extensive code. The large variety of data involving transactions, supply chains, and customers, required domain knowledge during cataloging and transformation according to the destination specifications.
Another major challenge was building pipelines that supported the processing and monitoring of real-time data such as transactions and website activities. Transaction auditing and real-time customer insight analysis needed to be accomplished upon streaming data. When data is coming from hundreds of facets from online and physical stores, it would take a lot of engineering bandwidth to aggregate the data and ensure its quality.
Nexla bridges the gap between data generation and data analytics by replacing the most tedious and time-consuming part of data engineering. The retail chain was able to build 500 pipelines within 90 days, freeing up over half of the resources that used to be spent on getting data ready for analytics. Domain experts can now take ownership and get the right data in the right format without going to the engineers, thanks to the no-code/low-code self-service UI.
- Unlimited connectivity: Nexla provides all the connectors the firm needs. A data analyst simply goes on the platform, chooses the source and destination, specifies transformation/validation, and then the data can instantly start flowing. Applications such as Marketo, Hubspot, Convey, and even emails can be set as the data source. Data engineers at the firm no longer need to custom build or rely on tools that only take care of fragments of the integration needs; instead, all the connectors are in one place meaning that scaling pipelines is simple as ever.
- Low-code, high customizability: Nexla helps decentralize the once-monolithic data operations at the enterprise by providing no-code/low-code tools that all data users can leverage. Data analysts use Nexla’s schema editor to join datasets and get specific slices of data via several clicks or snippets of code. Data transformation is also made intuitive: besides the pre-set logic, analysts can apply rules and validations within minutes, ensuring the incoming data fits the target schema. The transformations and schema templates can also be saved, edited, and shared among collaborators.
Nexla empowers data engineers at the company to build data pipelines in a way that is highly scalable, replacing outdated and fragmented tools and paving a smooth way for all future integrations. The data engineers can then focus on more value-driving projects and delegate domain-specific data tasks to analysts. This creates a shift from a centralized and monolithic data operations structure to a domain-driven data-as-a-product architecture where data is made accessible, readily usable, and shaped for diverse use cases for all data users. Data becomes actionable insights at a rate that is now faster than ever, meaning higher customer satisfaction and retention rate and better supply chain optimization.
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