Typical problems of agribusiness and Big data
Posted: Thu Jan 23, 2025 3:46 am
Among the databases in trend are Google BigQuery (due to the low cost of storage and processing) and Clickhouse (open source, originally developed by Yandex, therefore popular in our country), PostgreSQL (universal database) and Greenplum (a database created on the basis of PostgreSQL, but focused on analytics).
In organizing the process of collecting and processing peru telegram database data, alternatives to the long-standing Apache Airflow product have appeared: Dagster and Prefect.
For data processing, most often they use their own developments, often on the Pandas library. The DBT framework is gaining popularity, which allows solving all data processing tasks without involving programming languages.
Open source tools (Apache Superset, Redash, Metabase), the Russian Yandex DataLens system, the Chinese Fine BI, etc. are used to visualize data.
Big data analytics not only provides new opportunities, but also solves recurring problems that were considered endemic to the industry:
The agricultural sector is unfairly considered "slow and sluggish". When processes are managed exclusively manually, errors are inevitable due to excessive subjectivity, loss of time when information passes through the administrative chain and low readiness for rapid changes. Big data allows you to quickly monitor the situation and make balanced, informed decisions, predict their consequences and take risk factors into account. For example, one of the divisions of the world-famous giant agroholding Monsanto gives its farmers detailed visualization of processes based on data on food market needs, which allows them to make effective decisions and optimize production in real time.
The situation when the output of products is lower than planned and does not cover the needs is not uncommon in the agricultural sector. No less common is the opposite option - there is more product than the market can "digest", or its cost is too high, and a problem with sales may arise. With the help of Big Data, a manufacturer can not only predict production volumes with a high degree of accuracy, but also plan business development in the long term.
In organizing the process of collecting and processing peru telegram database data, alternatives to the long-standing Apache Airflow product have appeared: Dagster and Prefect.
For data processing, most often they use their own developments, often on the Pandas library. The DBT framework is gaining popularity, which allows solving all data processing tasks without involving programming languages.
Open source tools (Apache Superset, Redash, Metabase), the Russian Yandex DataLens system, the Chinese Fine BI, etc. are used to visualize data.
Big data analytics not only provides new opportunities, but also solves recurring problems that were considered endemic to the industry:
The agricultural sector is unfairly considered "slow and sluggish". When processes are managed exclusively manually, errors are inevitable due to excessive subjectivity, loss of time when information passes through the administrative chain and low readiness for rapid changes. Big data allows you to quickly monitor the situation and make balanced, informed decisions, predict their consequences and take risk factors into account. For example, one of the divisions of the world-famous giant agroholding Monsanto gives its farmers detailed visualization of processes based on data on food market needs, which allows them to make effective decisions and optimize production in real time.
The situation when the output of products is lower than planned and does not cover the needs is not uncommon in the agricultural sector. No less common is the opposite option - there is more product than the market can "digest", or its cost is too high, and a problem with sales may arise. With the help of Big Data, a manufacturer can not only predict production volumes with a high degree of accuracy, but also plan business development in the long term.