Name: 10 Best Practices for Data Engineers
Start: 2020-04-23T17:00:00Z
End: 2020-04-23T17:00:50.000Z
Location: BrightTALK
Rating: 4.2

Snowflake’s cloud data platform delivers the performance, simplicity, flexibility and concurrency needed for any data workload across any cloud.

One of the toughest challenges data professionals face today is streaming data for real-time analytics. A main barrier to real-time insights remains the complexity of the data itself, where companies do not have the tools and infrastructure to ingest and process structured and unstructured data. Organizations today need a data warehouse that is able to handle all data types and scale quickly to address growth, without the cost and burden of traditional data warehouses.

Join this webinar to learn how you can:
●Use a data warehouse to manage complexity when integrating different types of data from multiple sources
●Address data growth that surpasses storage and compute limitations
●Access real-time insights to offer better customer service, supply chain efficiencies, and operational improvements

Why You Need to Use Streaming Data for Real Time Analytics

Many organisations build data lakes with the goal of analysing their data to make data-driven decisions that improve their profitability or fulfill their corporate mission. However, the reality is many data lakes end up being a great place to cheaply store data but don’t achieve their initial goal due to excess complexity and an inability to conduct simple, fast data analytics.

Join our webinar “The Cloud Data Platform for Modern Data Lakes“ to learn more about how a modern data lake:

-  Acts as both a data lake and a data warehouse 
-  Offers unlimited speed and scale and allows all users to query all the data at any time
-  Stores and transforms raw data for analytical workloads
-  Is available as a simple service with minimal maintenance or complexity
-  Enables data science and data sharing initiatives

Plus learn about an EMEA data lake customer story.

The Cloud Data Platform for Modern Data Lakes

Are you an independent software vendor (ISV) or are you building a SaaS startup? Whether your data applications live in the cloud or on-premises, the underlying infrastructure and architecture is what will determine its scale, efficiency, and flexibility—and whether your app will gain traction or not.

Snowflake provides builders and developers of data-driven applications a ready-made data platform to build solutions that meet the demands of today’s organizations and their customers. 

Join Megan Schoendorf, Senior Software Engineer at Snowflake, for a live webinar and Q&A session, where she will reveal:

- Common architectural patterns for data applications
- How SaaS providers use Snowflake to deliver value-differentiated applications to their customers
- How Snowflake delivers a near-zero management development platform so you can develop massively scalable apps without operational overhead

Developing Data Apps with CI/CD and Service-Oriented Architecture

Many organizations like the structure provided by a data warehouse, yet they also want the flexibility a data lake provides. As a result, organizations often are forced to choose only one architecture method as their central data repository. Snowflake is challenging conventional thinking. Why can’t organizations have both a data lake and a data warehouse using one technology?

In this webinar, you will learn how to use Snowflake’s unique, cloud-built data platform that:

•  Provides the flexibility of data lakes, including schema-on-read

•  Allows organizations to build structured data models even from semi-structured data

•  Enables you to seamlessly analyze structured or semi-structured data, statically and as it continuously evolves

•  Eliminates management complexity with a software-driven service requiring minimal maintenance This powerful, yet simple data platform can significantly improve your time-to-insight from data.

Data Warehouse or Data Lake? How You Can Have Both in a Single Platform

Data engineering is one of the fastest growing jobs in the United States. In fact, in many organizations data engineers are essential for guiding data strategy and pipeline optimization to help businesses leverage their data for increased profitability and growth. 

If you’re a data engineer looking to make the right decisions about data strategies and tools for your organization, join our live webinar, as we discuss 10 best practices for data engineering.

This webinar will cover:  

- Tips for streamlining and operationalizing pipeline development

- The benefits of using data streaming instead of batch ingestion

- The types of tools to invest in

- Skills to help your organization harness the power of data

10 Best Practices for Data Engineers

data engineering

Apache Kafka

data pipelines

continuous data ingestion

SQL server

Data Architecture

Data Analytics

Data Management

Analytics

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10 Best Practices for Data Engineers

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