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As a data DSL, SQL offers very clear data lineage: since each statement has clearly defined data inputs and outputs, humans-and visual tools-can easily map out the “pipelines” of data through a set of SQL statements.Technically, these are “imperative” languages. By contrast, a traditional programming language like Java focuses on “how” the program behaves-the step-by-step description of algorithms. The technical description is that SQL is a “declarative” language. Every statement centers on two issues: what data is being accessed and what data is being produced. SQL focuses on “what” you want from your data rather than “how” computers should get what you want.The narrow scope might seem to limit potential, but the focus of the language offers several benefits. Relative to a general-purpose language like Java, SQL is a “domain-specific language” (DSL), focused primarily on one task: querying and manipulating data.
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Q: Why is SQL a good fit for Data Engineering?Ī : There are four key reasons SQL is a strong fit for data engineering: SQL is “just another language” that data teams can choose to work with, not a commitment to administering a complex software stack.
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#Does snowflake pro software require java code#
It’s easier for tools to interpret what the code is doing, and it illuminates how data is used and governed. Said differently, SQL data warehouses are now far easier to deploy and manage than in the days of Oracle’s dominance. Essentially, cloud data warehouses are infinitely scalable and allow flexibility in pricing to meet business needs as they evolve. The heavy lifting is all taken care of by the cloud vendors, including scaling databases as they change over time. Now, businesses can set up a small database in the cloud, and it can grow as big as needed. The Cloud provided SQL data warehouses with two key properties: ease of getting started and scaling up and down.īefore the cloud, databases had barriers of being hard to manage or manage at scale. This means SQL has a pretty vital role to play.Ī: In two words: The Cloud. The truth is that outside Silicon Valley, there historically haven’t been many organizations with long-term software engineering groups focused on data. SQL-centric shops do not write nearly as much custom code and instead emphasize SQL skills. Java-centric shops need software engineers who are good at writing and maintaining custom Java code. One main issue is the skills and proclivities of your team. The comparison of the two “shops” inevitably leads to the question: which is better? There are pros and cons to emphasizing one or the other. Today, SQL seems to be the focus of every data engineering conversation and popping back up on billboards in Silicon Valley. With cloud data warehouses enabling scale and flexibility, it’s easier than ever to embrace ELT with SQL.įor the first decades of the Millenium, it seemed like the Java-centric approach was the “hot new thing,” but SQL has been roaring back. SQL aligns well with ELT, Extract-Load-Transform, where the steps are similar to ETL but ordered differently so that Transformation is done in SQL. Most cloud data warehouses, such as Snowflake, Amazon Redshift, BigQuery, and Azure SQL Data Warehouse support SQL, as do many traditional database engines including Oracle, MySQL, PostgreSQL, and Teradata. On the other hand, SQL is a language specifically for manipulating data. In this paradigm, you extract data from many sources, transform it using various tools, often Java/Scala-based, and then load the transformed data into a data warehouse for analysis or machine learning work. Java aligns well with ETL: Extract-Transform-Load. Many open-source “big data” solutions in the last decade, such as Hadoop, Spark, Kafka, Storm, and Scalding, are designed for Java/Scala programmers. Java (and its younger sister Scala) is a general-purpose programming language for software engineers. Q: Joe, we hear you talking about 2021 as the “Summer of SQL.” What do you mean by that?Ī: Broadly speaking, there are two types of Data Engineering shops: Java-centric and SQL-centric.
#Does snowflake pro software require java series#
This is the first in a series of chats on SQL’s role in today’s data engineering. To find out why and what we missed while it was gone, we asked a few questions of Joe Hellerstein, a computer science professor at UC Berkeley and co-founder of Trifacta. SQL is back in a big way this summer, after what seemed like a period of time in the back seat.