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From Zero to ETL Hero-A-Z Guide to Become an ETL Developer

ProjectPro

Data Integration and Transformation, A good understanding of various data integration and transformation techniques, like normalization, data cleansing, data validation, and data mapping, is necessary to become an ETL developer. Data Governance Know-how of data security, compliance, and privacy.

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Top 16 Data Science Job Roles To Pursue in 2024

Knowledge Hut

They use technologies like Storm or Spark, HDFS, MapReduce, Query Tools like Pig, Hive, and Impala, and NoSQL Databases like MongoDB, Cassandra, and HBase. They also make use of ETL tools, messaging systems like Kafka, and Big Data Tool kits such as SparkML and Mahout.

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What Is Data Engineering And What Does A Data Engineer Do? 

Meltano

Their tasks include: Designing systems for collecting and storing data Testing various parts of the infrastructure to reduce errors and increase productivity Integrating data platforms with relevant tools Optimizing data pipelines Using automation to streamline data management processes Ensuring data security standards are met When it comes to skills (..)

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What is Data Extraction? Examples, Tools & Techniques

Knowledge Hut

Database Queries: When dealing with structured data stored in databases, SQL queries are instrumental for data extraction. SQL queries enable the retrieval of specific data subsets or the aggregation of information from multiple tables. Cleaning and validating data during the extraction process is crucial but can be challenging.

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What is Data Integrity?

Grouparoo

If inadequate quality data enters a process, then any integrity change will not affect the quality of the data, just its correctness. Ensuring good data quality is a separate topic from maintaining good data integrity. Why is Data Integrity Important? Data integrity is one of the triads of data security.

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10 Essential Azure Data Engineer Skills to Improve in 2023

Knowledge Hut

Cloud Platform Skills A strong grasp of Microsoft Azure, covering a spectrum of services for seamless deployment, scaling, and management of data solutions, leveraging the power of the cloud. Data Integration and ETL Tools As an Azure Data Engineer, master data integration and ETL tools crucial for seamless data processing.

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What Is Data Wrangling? Examples, Benefits, Skills and Tools

Knowledge Hut

Outlier Detection: Identifying and managing outliers, which are data points that deviate significantly from the norm, to ensure accurate and meaningful analysis. Fraud Detection: Data wrangling can be instrumental in detecting corporate fraud by uncovering suspicious patterns and anomalies in financial data.