This can also be used to store static content on web and also used as fast layer in lambda architecture. This service improves performance of the web applications by allowing to store information in in-memory cache and then retrieve information from fast in-memory caches, instead of making multiple trips to slower backend databases. Amazon ElastiCache Amazon ElasticCache is managed service that supports Memcached and Redis implementations. It provides sub milliseconds response times. Cassandra is very good for application which have very high throughput and supports faster reads when queries on primary or partition keys. Cassandra Managed service Amazon Managed Apache Cassandra Service is a scalable, highly available, and managed Apache Cassandra–compatible database service. It can also be used to store unstructured data, content and media, backups and archives and so on. It can be used in place of HDFS like your on-premise Hadoop data lakes where it becomes foundation of your data lake. It is a distributed, high-scalable, high available cloud storage. When it comes to Cloud, my experience is it’s better to use cloud native tools mentioned above should be suffice for data lakes on cloud/Īmazon S3 Amazon Simple Storage is a managed object store service provided by AWS. There are several data governance tools available in the market like Allation, Collibra, Informatica, Apache Atlas, Alteryx and so on.
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MDM also deals with central master data quality and how to maintain it during different life cycles of the master data. There are lot of MDM tools available to manage master data more appropriately but for moderate use cases, you can store this using database you are using. As this data is very critical, we will follow type 2 slowly changing dimensional approach which will be explained my other blog in detail. This will also provide a single source of truth so that different projects don't show different values for the same. This will help you to avoid duplicating master data thus reducing manageability. This data will be shared among all other projects/datasets. Data Quality and MDM Master data contains all of your business master data and can be stored in a separate dataset.
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How data was modified or added (storing update history where required - Use Map or Struct or JSON column type). Who updated the data (data pipeline, job name, username and so on - Use Map or Struct or JSON column type)? 3. Data last updated/created (add last updated and create timestamp to each row). The following are some examples of data lineage information that can be tracked through separate columns within each table wherever required. Most of the Big Data databases support complex column type, it can be tracked easily without much complexity. Some of the Data lineage can be tracked through data cataloging and other lineage information can be tracked through few dedicated columns within actual tables.
Data Lineage There is no tool that can capture data lineage at various levels. Auditing It is important to audit is consuming and accessing the data stored in the data lakes, which is another critical part of the data governance. Data Discovery It is part of the data cataloging which explained in the last section.
Please refer to my blog for detailed information and how to implement it on Cloud. Data Cataloging and Metadata It revolves around various metadata including technical, business and data pipeline (ETL, dataflow) metadata. Please visit my blog for detailed information and implementation on cloud. Security Covers overall security and IAM, Encryption, Data Access controls and related stuff. It involves lot of things like security and IAM, Data cataloging, data discovery, data Lineage and auditing. Data Governance on cloud is a vast subject.
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