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Apache Kafka Vs Apache Spark: Know the Differences

Knowledge Hut

Spark Streaming Kafka Streams 1 Data received from live input data streams is Divided into Micro-batched for processing. processes per data stream(real real-time) 2 A separate processing Cluster is required No separate processing cluster is required. it's better for functions like row parsing, data cleansing, etc.

Kafka 98
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Top 12 Data Engineering Project Ideas [With Source Code]

Knowledge Hut

If you want to break into the field of data engineering but don't yet have any expertise in the field, compiling a portfolio of data engineering projects may help. Data pipeline best practices should be shown in these initiatives. In addition to this, they make sure that the data is always readily accessible to consumers.

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Data Lake Explained: A Comprehensive Guide to Its Architecture and Use Cases

AltexSoft

The term data lake itself is metaphorical, evoking an image of a large body of water fed by multiple streams, each bringing new data to be stored and analyzed. Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture.

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AWS Instance Types Explained: Learn Series of Each Instances

Edureka

Introduction to AWS Instance Types Amazon Web Services (AWS) offers a diverse range of instance types, each tailored to specific computing needs and optimized for various workloads. In-Memory Caching- Memory-optimized instances are suitable for in-memory caching solutions, enhancing the speed of data access.

AWS 52
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When To Use Internal vs. External Stages in Snowflake

phData: Data Engineering

Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. Snowflake hides user data objects and makes them accessible only through SQL queries through the compute layer. These stages are unique to the user, meaning no other user can access the stage.

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Big Data Analytics: How It Works, Tools, and Real-Life Applications

AltexSoft

Data generated from various sources including sensors, log files and social media, you name it, can be utilized both independently and as a supplement to existing transactional data many organizations already have at hand. Data storage and processing. Data cleansing. whether small or big ? NoSQL databases.

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20+ Data Engineering Projects for Beginners with Source Code

ProjectPro

Thus, as a learner, your goal should be to work on projects that help you explore structured and unstructured data in different formats. Data Warehousing: Data warehousing utilizes and builds a warehouse for storing data. A data engineer interacts with this warehouse almost on an everyday basis.