AWS Big Data

MARCH 20, 2023

Unlike simpler data file formats such as Apache Parquet, CSV, and JSON, which can store big data, data lake frameworks organize distributed big data files into tabular structures that enable basic constructs of databases on data lakes. For S3 URL , enter s3://noaa-ghcn-pds/csv/by_year/2022.csv.

Visualization 77

Visualization Data Lake Snapshot Big Data 77

IBM Big Data Hub

APRIL 15, 2024

This is a nearly 10% increase in complaints and 22% increase in losses compared to 2022. When a cyberattack strikes, the ransomware code gathers information about target networks and key resources such as databases, critical files, snapshots and backups.

Snapshot 107

Snapshot Machine Learning Interactive Statistics 107

AWS Big Data

APRIL 27, 2023

It supports modern analytical data lake operations such as create table as select (CTAS), upsert and merge, and time travel queries. Athena also supports the ability to create views and perform VACUUM (snapshot expiration) on Apache Iceberg tables to optimize storage and performance. Create a table to point to the CDC data.

Data Lake 71

Data Lake Snapshot Optimization Data Transformation 71

AWS Big Data

OCTOBER 19, 2023

Name this new job hudi-data-ingestion. Under Visual , choose Data source – S3 bucket. For S3 URL , enter s3://noaa-ghcn-pds/csv/by_year/2022.csv. The data source is configured. The next step is to configure the data target to ingest data in Apache Hudi on your S3 bucket. Choose Data target – S3 bucket.

Data Lake 111

Data Lake Data Warehouse Visualization Snapshot 111

AWS Big Data

MAY 30, 2023

resource(“dynamodb”) table = dynamodb.Table(dydb_lookup_table) response = table.scan() items = response[“Items”] jsondata = sc.parallelize(items) lookupDf = glueContext.read.json(jsondata) return lookupDf # Load the Amazon Kinesis data stream from Amazon Glue Data Catalog. def readDynamoDb(): dynamodb = boto3.resource(“dynamodb”)

Data Lake 77

Data Lake Data Analytics Analytics Data Processing 77

AWS Big Data

JUNE 28, 2023

Amazon Aurora zero-ETL integration with Amazon Redshift was announced at AWS re:Invent 2022 and is now available in public preview for Amazon Aurora MySQL-Compatible Edition 3 (compatible with MySQL 8.0) Analyze the near-real time transactional data Now we can run analytics on TICKIT’s operational data.

Data Warehouse 83

Data Warehouse Analytics Metrics Dashboards 83

AWS Big Data

NOVEMBER 1, 2023

Originally published on December 9th, 2022. Amazon Redshift is a fully managed, petabyte scale cloud data warehouse that enables you to analyze large datasets using standard SQL. Choose the Maintenance Select a snapshot and choose Restore snapshot , Restore to provisioned cluster. See the required steps as below.

Data Warehouse 93

Data Warehouse Snapshot Testing Management 93

AWS Big Data

JUNE 9, 2023

Despite these capabilities, data lakes are not databases, and object storage does not provide support for ACID processing semantics, which you may require to effectively optimize and manage your data at scale across hundreds or thousands of users using a multitude of different technologies.

Data Lake 113

Data Lake Metadata Optimization Statistics 113

AWS Big Data

MARCH 3, 2023

You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes. Amazon EMR Serverless is a serverless option in Amazon EMR that makes it easy for data analysts and engineers to run open-source big data analytics frameworks without configuring, managing, and scaling clusters or servers.

Data Lake 80

Data Lake Dashboards Metrics Metadata 80

Smart Data Collective

AUGUST 21, 2022

Big Data and AI are, perhaps, the most important business technologies of the century, and they are intrinsically related. But what is the state of AI and Big Data, right now? But what is the state of AI and Big Data, right now? Big data and AI have what is referred to as a synergistic relationship.

Big Data 86

Big Data Cost-Benefit Sales Snapshot 86

AWS Big Data

NOVEMBER 29, 2023

We have seen a strong customer demand to expand its scope to cloud-based data lakes because data lakes are increasingly the enterprise solution for large-scale data initiatives due to their power and capabilities. About the authors Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team at Amazon Web Services.

Data Lake 102

Data Lake Management Metrics Data Warehouse 102

AWS Big Data

JUNE 21, 2023

Extract data from DynamoDB To extract the data from DynamoDB to our S3 data lake, we use the new AWS Glue DynamoDB export connector. Unlike the old connector, the new version uses a snapshot of the DynamoDB table and doesn’t consume read capacity units of your source DynamoDB table.

Data Warehouse 78

Data Warehouse Data Lake OLAP Cost-Benefit 78