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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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SAS- Get the Right Training, Go get the Job

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sas career options

sas career options
Many people think analytics is about gathering data using software tools and creating dashboards and reports. However, analytics is much more. Analytics goes beyond data; its primary goal is to enable business decisions based on that data. This involves working with stakeholders to understand the gaps in the business and using this knowledge as a guide to manipulate data, derive useful insights, and make recommendations – all key actions to increase revenue and lower costs.
Wherever you sit in your organization, what’s most important is the bottom line. And so whether you lead business or IT units or are in the trenches, the analytics profession has likely crossed your mind. What does it entail? Who are true analysts? How does one become an analyst?
Those of you specifically in a data management, data warehousing or business intelligence role may wonder how to further develop your analytics career. On the surface, an “analytics career” can be quite broadly defined, and the transition to it can seem very confusing. However, the structured approach we describe in this article will make it easy to choose your path – and give managers and leaders an appreciation for the developmental steps to success.

Don’t expect to learn analytics from blogs and social chatter. There is a lot of information published online. Do your own due diligence.
Don’t view conferences as a solution for training

Analytics is hot field- many jobs available. To land a great analytics job, consider networking via LinkedIn. Use LinkedIn Jobs as well as LinkedIn analytics groups and highlight your analytics skills using tags. Also consider key job portals, such as Craigslist, icrunchdata, Indeed, Dice and Monster.

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