IBM Journey to AI blog

NOVEMBER 3, 2023

It allows your business to ingest continuous data streams as they happen and bring them to the forefront for analysis, enabling you to keep up with constant changes. Apache Kafka boasts many strong capabilities, such as delivering a high throughput and maintaining a high fault tolerance in the case of application failure.

Apache Kafka 79

Apache Kafka Data Warehouse Data Pipeline Big Data 79

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

DECEMBER 9, 2024

What is Apache Hive? Hive is a data warehouse tool built on Hadoop that enables SQL-like querying to analyse large datasets. What is the Difference Between Structured and Unstructured Data? What is Apache Kafka, and Why is it Used? Explain the CAP theorem and its relevance in Big Data systems.

Big Data 52

Big Data Big Data Hadoop Apache Kafka 52

Pickl AI

AUGUST 9, 2024

Apache Spark A fast, in-memory data processing engine that provides support for various programming languages, including Python, Java, and Scala. Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JULY 8, 2024

Handling Missing Data: Imputing missing values or applying suitable techniques like mean substitution or predictive modelling. Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Pickl AI

JULY 29, 2024

Limited Support for Real-Time Processing While Hadoop excels at batch processing, it is not inherently designed for real-time data processing. Organisations that require low-latency data analysis may find Hadoop insufficient for their needs.

Hadoop 52

Hadoop Clustering Big Data Big Data 52

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. Strong community and tech support.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59