3 Ways Data Engineers Can Deal With Enterprise Data Pipelines
3 Ways Data Engineers Can Deal With Enterprise Data Pipelines
We discuss how to make the most out of your data pipeline by using a BI tool that makes your job easier, and contributes an even bigger impact on the organization.
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Data engineers are considered the real builders in the data world today, and one of the main reasons is that they help organizations get value out of their data. For an enterprise company, that can mean building and maintaining data pipelines or optimizing database queries and anything in between.
If you are a data engineer, then you know that data is your most valuable asset. The aged statistic still stands that 80% of your time will be spent preparing and optimizing data.
This is not as easy as it sounds. You need to find the right data sets, clean them up, and test out interoperability. But you also need to deliver comprehensible insights, make changes on the fly, and continue to deliver the most up-to-date information from the latest data available. And with tight deadlines and decisions that need to be made, you need to set up your infrastructure to simplify all this complexity, be easy to use, and lock down tight on security and user permissions.
Problem? Not if you have the right BI platform in place.
Simplify All That Complexity
Enterprise companies are naturally complex. This goes way back to the 1990s when the big companies realized that they had way too many places where their data was sitting, so they started to introduce enterprise resource planning or ERP systems that centralized all their data sources. By the late 1990s, it was estimated that businesses around the world spent $10 billion per year on these types of enterprise systems. It was a big investment that is still viable, and one that forms the legacy databases of many companies today.
Fast forward to Big Data. Now we can throw in the four 4 Vs of Big Data (Variety, Volume, Velocity, and Veracity) and compound the data issues of the enterprise with an even bigger data issue. So how can data engineers deal with both the historical and the current data landscape of huge organizations?
The right tools and platforms that are easy to deploy, play well with legacy and modern systems, and can manage a complete end-to-end BI process, are what modern data engineers need in order to fully embrace complex data.
The Right One
We have often talked about the single-stack approach to business analytics, and with the complexity of enterprise data, this approach makes even more sense.
You want to make sure you have one place to bring in all your data and do your data modeling. This eliminates the need to do your data modeling on a separate platform (part of simplifying is getting rid of all those extra layers). Everything is achieved on a single integrated platform, without requiring specialized IT resources and massive amounts of time.
Consolidating into one integrated tool for prep, analysis, and visualization gives you and your team more functionality all in one place. You can eliminate the frustration that you and your users experience when working across multiple and disconnected tools. Working in this way promotes higher user satisfaction both in terms of data relevance and the ease of overall access.
Bottom line: with a single-stack BI platform, you have an integrated tool that simplifies analytics for complex data, making your data-heavy tasks much easier to implement and much faster to complete. What's more, you'll also be promoting high levels of analytical activity throughout your organization, and that means you're on your way to a data-driven culture in teams, departments, and all levels of management.
Connect to it All
As a data engineer, you need to build and manage data pipelines. This starts with connecting to the data, and connecting to data can be a nightmare when you have so many disparate data sources. The key here is data connectors. You need data connectors for nearly every database — including generic ODBC, JDBC, and REST API connectors — enabling you to get up and running fast with all the data, regardless of where it is located. Once you get connected, there are a few ways you can access and work with your data:
Query Data Live
More than likely, you are running and maintaining a high-performance data warehouse, such as Snowflake, Amazon Redshift, or Google BigQuery. In this case, you may want to connect live to these sources. A live connection means that changes made to the data in the source system are reflected immediately to your end users.
In other words, you can connect to all your data and power your analytics live from the source. This keeps your data pipeline fresh, up-to-date, and ensures that there is never a disruption in your data pipeline that could slow down insights for your end users.
Build Cached Models
Enterprise companies usually have legacy systems that contain important data. You can use your BI platform to pull this data from these data warehouses into the BI platform, then build accelerated models inside the platform that can be analyzed. Then, you can put your live data model next to your cached data model, side by side. Chances are it's a view your users have never seen before, giving them a much broader, deeper perspective on their entire data landscape.
Now Go Hybrid
The modern way to deliver the best insights to your end users is to use a hybrid approach: connect and query your data directly from the cloud, and also mashup data and build performant cache data models in your BI platform.
This is the best of both worlds. A hybrid solution that feeds live data and updates into your dashboards directly from external databases while combining dashboards and analysis from historical data in the system.
Also, this is a great way to leverage the power of your legacy data warehouse, as you are still extracting data from these sources, manipulating it, and creating data models directly inside the BI platform, while connecting live to other sources of data.
Technological evolutions like these mean you can embrace lightning-speed, live connection access to data in a Redshift environment, making ad-hoc and complex queries easier and more accurate than ever, putting it together with your legacy systems for a more complete, 360-degree view.
Speaking Your Language
Now that you have all the infrastructure in place, you'll want to make sure you can write code that gives the data analysts and data scientists a reliable base for their inquiries.
By using simple SQL language, you can query your sales figures and come up with a data pipeline that any executive can use to further manage the business. You can unify the data from Finance, Sales, Marketing, how many units were sold, what is the most popular product, etc. The combinations are endless, That's what's so great about SQL. You can use it for so many different ways of modeling your data.
If you are ready to prepare and analyze your data in code, you can add R and Python to visualize the information. Most mature data teams today use the skill sets of multiple languages in order to transform the data in new ways and uncover new insights. Using SQL for the heavy lifting, and R and Python to complete and enhance the data makes for a serious competitive edge that every enterprise company needs, no matter what the industry or service.
Control Scale and Cost
According to CNN Money, the total number of employees among all Fortune 500 companies is 26,405,144 people. This makes the average number of employees per firm 52,810 people. For enterprise data engineers, that adds up to a lot of data queries. So how can you scale your BI while controlling the cost?
With the cost of querying your data is dependant on the number of queries you make in your data warehouse, one of the most effective ways to control this cost is to pull the data out of the data warehouse and do the querying inside your BI platform. In an enterprise environment, this can be a huge cost saving.
You can also test and monitor usage inside the BI tool as you scale so that you can know and plan the costs up front. And with enterprise companies focused on increasing profit, keeping query costs down will go a long way towards building a better ROI for your data pipeline.
Lock it Down
You've heard it before, and yes, we will say it again, security is the most important consideration when creating data pipelines that will be sent to the entire organization.
Starting with the data connectors, make sure you are encrypting the login information. For example, if you are connecting to a live data source such as Amazon Redshift, recent login information may be automatically populated. We suggest encrypting the connection for an additional layer of security to prevent any other builder or user accessing your data.
You should be able to regulate all your users, data, and settings from one UX panel. Then, you can grant your users access for such things as data modeling, building, or viewing, and restrict access according to individual or group. This will keep everyone's data secure as you share data across the organization, and add, delete, and create groups of users.
Usage analytics is a great way to learn all about group activity, platform usage, dashboard viewing patterns, traffic, and user interactions with your data pipelines. You will be able to keep close control over your users' activity and minimize unwanted surprises.
Serving Up Insights
For high-performance and scale, your BI tools need to sit on top of your data warehouse. From there, you can streamline the data access, analysis, and usage monitoring, from one single platform.
All of these pieces we've talked about are designed to support teams of developers, analysts, and end users so they can use your data pipelines to build BI apps and solve problems without bottlenecks and slowdowns.
Make the most out of your data pipeline by using a BI tool that makes your job easier, and contributes an even bigger impact on the organization.
Published at DZone with permission of Dana Liberty , DZone MVB. See the original article here.
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