Data engineers have always encountered challenges that speak to the warehousing, visualization, processing, security and querying dynamics of the day.

Not so long ago, storing large quantities of data on premises was a key challenge, but the emergence of cloud services has put an end to that. However, other issues exist. No matter the industry, data science and engineering seem to face the same issues over and over again, only manifested in new ways that reflect the trends of our time.

In this article, we’ll explore these issues and the tools that can help you overcome them.

Metadata management

While data storage isn’t an issue anymore, organizing vast quantities of data is a challenge. The volume of data is increasing daily, and this makes metadata organization and management critical. Descriptions of datasets, the purpose of tables and schemas, and data quality issues have to be tracked and regularly updated.

Currently, many organizations use manual processes to organize, share, and link metadata. A metadata management tool such as erwin’s Data Intelligence Suite (DI suite) helps companies organize their metadata and keep track of historical changes. The tool helps automate data management and governance.

Tracking metadata changes when disparate data sources are involved in challenging. Data sources don’t always talk to one another, and transforming data can be challenging. The DI suite can harvest and transform data from multiple sources into a central data catalog. Thanks to role-based visibility, access to metadata is secure and audited.

Establishing data lineage is a key part of metadata management. Every organization has to track how data in their possession is being shared and for what purposes to satisfy compliance requirements. Using data for purposes other than the ones indicated has serious consequences in the age of GDPR and other data privacy laws.

Columns containing sensitive and personally identifiable information have to be tagged in advance. Doing this demonstrates an organization’s ability to adhere to “right to be forgotten” requests. The DI suite helps you track and categorize your data sources, as well as tag sensitive sources of data. With clear audit trails that record transformation processes, your organization will be able to record and validate all of its data sources with ease.

Integrating multiple data systems

In today’s workplaces, different teams rely on different tools. Each storage and organization solution has workflows it works best with. It’s highly unlikely that an organization will run a single data storage and analytics platform.

The best data platform integrates multiple data sources and allows you to easily democratize that data through dashboards. Ad-hoc self-service analytics are essential for businesses to be able to maintain their competitive edge. The days of relying on IT departments to run complex queries and create standard reports are long gone. Today, line-of-business users can upload their own data sources, and AI prepares it and maps all the connected fields in autopilot.

Sisense’s data analytics platform allows organizations to easily integrate data from various sources, and also allows you to query these data in a visual platform. Best of all, data points can be labeled in business-friendly ways that support data democratization initiatives.

Standardizing a single solution is unfeasible, and this is why an integration-friendly platform is essential. Sisense can integrate with different legacy sources of data and transform data whether it’s EOD or real-time, using native connectors. Creating a data movement pipeline and installing alert systems is simple thanks to Sisense’s ability to integrate directly with your ETL tools. And using Sisense’s developer tools, product teams can embed its capabilities in any app.


Scaling machine learning reporting processes is easy when you have a small team. In these environments, ad-hoc processes work best. However, they don’t scale to serve large enterprises. Bringing engineering rigor to these workflows is essential. Software engineering teams employ source control, code reviews, and continuous integration, but teams rarely migrate this framework to non-engineering disciplines.

Creating DevOps workflows and applying them to analytics and ML will help reduce code volume and bring production level rigor to workflows. The best tool to utilize is perhaps Azure DevOps which allows you to install pipelines that can deploy analytics and ML from git to production. Azure Pipelines helps you deploy code from source control, while tools such as Azure Monitor can help you track deployment efforts using real-time alerts.

You can use the tool to deploy to any cloud, and it contains CI/CD pipelines for every open source project. Bringing DevOps workflows to analytics isn’t something you can do overnight. However, installing DevOps processes will help you bring self-service analytics to your organization quickly, and securely.

Data quality

The quality of your analytics processes depends on the quality of your data. There are six aspects of data quality that you need to track at all times. First, you need to make sure your datasets are complete and aren’t missing important data. Data consistency is the next key characteristic you need to monitor.

Transforming data efficiently so that it can be used across your organization consistently is crucial if you want to democratize it. The transformation process often creates gaps in data integrity. Your data quality tool has to make sure all relationships are maintained across datasets. Lastly, your data should be available when you need it, as expected, without delay.

A data platform such as OpenRefine helps you achieve these objectives with ease. You can explore, clean, reconcile, and match data across various web services. Use your analytics solution to monitor the efficiency of your data quality processes by integrating it with your data quality tool.

A tool for every challenge

While issues will always crop up in data and analytics engineering, the tools highlighted above will allow you to move past them with ease. As the scope of data engineering grows, there will be more challenges and better tools to deal with them.