The world of data operations is getting more complex each day. To manage the complexities of operations, you need to make sure that you are using the right tools for the operations. Many of these tools require programming languages. To dive deeper, Lyftrondata conducted a poll to know the most preferred language for heavy data operations.
According to the poll, Python was chosen as the most preferred language with 79% votes. The second position was bagged by Java with 10% votes. ‘Others’ were in the third position followed by Scala being #3.
Make better and quicker data migration decision
What is Python?
Python is a powerful, flexible, and highly extensible programming language. It is often used as a scripting language but is also used in a wide range of applications, including web development, numerical and scientific computing, software development, and system administration.
What is Java?
Java is an object-oriented, platform-independent, distributed computing platform. It is a software application, and it does not require installation. It was developed by Sun Microsystems and released in 1995. It is the programming language, and it is supported on more than 924 million devices. It is the most used language for the development of mobile applications.
What is Scala?
Scala is a mixture of Object-Oriented Programming and Functional programming. Scala has both OOPS and FP constructs which enable developers to work in the most suitable style depending on the requirements of the project.
What is Lyftrondata?
Lyftrondata, a modern data fabric platform solution, helps to load the data in Star Schema, provides real-time data access, and enables users to query them with simple ANSI SQL. With Lyftrondata, enterprises can build data pipelines in minutes and shorten time to insights by 75%, with the power of modern cloud computing of Snowflake and Spark.
Lyftrondata eliminates the time spent by engineers building data pipelines manually and makes data instantly accessible to analysts with simple and standard ANSI SQL. Its pre-built connectors automatically deliver data to warehouses in normalized, ready-to-query schemas and provide full search on the data catalog.
Key Differentiators of Lyftrondata
Create a data pipeline in minutes: Register over 100+ types of data sources in one place. Choose the most valuable data sources and replicate them to the cloud.
Power modern delta lake & data warehouse: Lyftrondata enables you to build a modern data warehouse and data lake in just a few clicks. Normalize all data sets and load the data to the data warehouse. Apply complex transformations with SQL when needed.
Shortens time-to-insights: Empower data-savvy users to find and prepare the data they need for analytics. Enable real-time access to any data source from any BI tool.
Unlimited compute: Lyftrondata enables you to compute on Databricks Spark and Snowflake. Thus, you have the flexibility to choose to compute on either of these modern platforms.
Integrate multiple clouds: Build a single view of data across different clouds and regions. Replicate data between different regions of the clouds and put them in sync.
Phase transition to the cloud: Migrate on-premise data warehouses to the cloud step-by-step. Create data pipelines for migrated data warehouses and legacy data warehouses in real-time. Not all data warehouses may be moved to the cloud in one step.
Build an agile data culture: Empower data users to find and prepare the data they need in analytics. A fast data strategy based on a combination of a modern data pipeline and real-time data saves delays in data preparation.
Ensure data governance in the cloud: Build a searchable data catalog of valuable data sources. Apply table, row, and column security to any data source on-premise, SaaS, or in the cloud. Build a governed data lake integrated with the enterprise active directory for authentication.
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