#79 - Data Transformations in 2022 w/ Armon Petrossian & Satish Jayanthi (Coalesce.io)
Apr 25, 2022
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Discover the evolving landscape of data transformations in 2022 and beyond with Coalesce.io. Topics include challenges in data transformations, evolution of Data Vault 2.0, balancing flexibility and efficiency in tools, and navigating collaborative structures for successful outcomes.
Coalesce.io focuses on efficient data transformations using automation principles to address bottlenecks in analytics.
Managing data transformations on powerful cloud platforms like Snowflake remains a challenge due to inefficiencies and increased costs.
Inefficient data architectures, lack of standardization, and documentation issues lead to delays and bottlenecks in the data transformation process.
Deep dives
Data Transformation Automation with Coales
Coales, a data transformation tool, focuses on solving bottlenecks in analytics by applying automation principles similar to tools like FiveTran for data access and Tableau for BI. The company targets issues like exponential data growth and the shortage of data engineers in the market, highlighting the need for efficiency and productivity gains in data transformations.
Challenges in Data Transformation Landscape
The emergence of powerful cloud platforms like Snowflake revolutionized analytics tools, but managing data transformations remained a significant challenge. While platforms like Snowflake simplified access to data, managing transformations and maintaining lineage required additional solutions, leading to inefficiencies and increased costs in the analytics landscape.
Data Transformation Bottlenecks in Analytics
Inefficient data architectures, lack of standardization, and documentation issues contribute to delays and bottlenecks in the data transformation process. The complexity of managing transformations and ensuring data quality can lead to prolonged project timelines, increased expenses, and the need for re-engineering data projects.
The Importance of Data Transformation Efficiency
Efficient data transformations play a crucial role in driving insights and value from data projects. Properly transforming raw data into actionable insights requires structured approaches like data modeling and adherence to standards. Understanding the business value of data engineering and analytics is essential for optimizing outcomes and productivity.
Future of Streaming Data and Data Modeling
The evolving data landscape is shifting towards real-time analytics and streaming data use cases. Companies are exploring tools and strategies for fast analytics and low latency queries to support real-time decision-making. Balancing historical data warehousing with streaming use cases and adopting data ops practices from the software engineering domain are key trends shaping the future of data transformations.
The data transformation tooling space is undergoing a sea change right now. Armon Petrossian & Satish Jayanthi from Coalesce.io join the show to talk about what's next in data transformations in 2022 and beyond.
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TERNARY DATA
We are Matt and Joe, and we’re "recovering data scientists". Together, we run a data architecture company called Ternary Data. Ternary Data is not your typical data consultancy. Get no-nonsense, no BS data engineering strategy, coaching, and advice. Trusted by great companies, both huge and small.
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